Management in the face of uncertainty. Summary: Intelligent control systems Control object of intelligent control systems

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Introduction

Intelligent control is a generalization of semiotic, cognitive and informational control. In intelligent transport management, the following areas are distinguished: intelligent transport systems, intelligent semiotic control and intelligent cognitive control. Intelligent semiotic control is associated with different forms of logic, production systems, evolutionary algorithms. Intelligent cognitive control is viewed as a synthesis of human computer control using associative channels and tacit knowledge analysis. Intelligent control is viewed as a means of making decisions under conditions of uncertainty. Intelligent information management is seen as supporting intelligent information technology management.

The need for intelligent control

With the development of society and the complication of objects and management tasks, management technologies also changed. The most acute problem in managing complex situations was the problem of "big data". It creates an information barrier for "organizational management" technologies. The growth of poorly structured information is characteristic of modern management. This leads to the transition to intelligent management, which, in turn, leads to the need to apply knowledge management technologies. Intelligent control is based on intelligent systems and intelligent technologies. An intelligent system is a technical or software-technical system capable of obtaining creative solutions to problems belonging to a specific subject area, knowledge about which is stored in the memory of such a system. Simplified, the structure of an intelligent system includes three main blocks - a knowledge base, a solver, and an intelligent interface. The solver is the dominant component of an intelligent system. In first-order logics, a solver is a mechanism for obtaining solutions to logical expressions. In multi-agent systems, which are classified as artificial intelligence, the concept of a solver is also used. An agent is a problem solver, which is a software entity that can act to achieve its goals. In symbolic modeling, an s-solver is a message specialization value. One of the first in Russia to introduce this concept was Efimov E.I. ... From this short list follows the importance of the solver for intelligent systems and intelligent technologies.

Intelligent control in the framework of applied semiotics

Semiotics studies the nature, types and functions of signs, sign systems and sign human activity, sign essence of natural and artificial languages ​​in order to build a general theory of signs. In the field of semiotics there is a direction "applied semiotics", the founder of which is D.A. Pospelov.

In semiotics, two areas of application of signs are distinguished: cognition and communication. This divides semiotics into two parts: semiotics of cognition; semiotics of semantic communications. The basis of intelligent control is the semiotic system. According to Pospelov, an ordered eight of sets is called a semiotic system W:

W =< T, R, A, P, τ, ρ, α, π>, (1)

where
T - set of basic symbols;
R - set of syntactic rules;
A - a lot of knowledge about the subject area;
P is a set of rules for deriving decisions (pragmatic rules);
τ are the rules for changing the set T;
ρ - rules for changing the set R;
α - rules for changing the set A;
π are the rules for changing the set P.

The first two sets generate the language of the system W, and τ and ρ carry out its change. rules α change a lot of knowledge about the subject area. If we consider knowledge as axioms of the formal system (which is formed by the first four elements of W), then the rules α , in essence, change the interpretation of the basic symbols and, consequently, correctly constructed formulas of the language of the semiotic system W.

The first four sets form the formal system FS, elements from the fifth to the eighth form the rules for changing the formal system. In this way, they ensure the adaptation of the formal system, "adjusting" it to solve problems and problems that within the system FS cannot be solved.

Thus, the semiotic system (1) can be defined as a composite dynamic system: W = , where FSi- determines the state of the semiotic system, and MFsi- the rule for changing its state. In this it should be noted that although we are talking about a semiotic system, de facto such a system describes the control object, that is, the state of the control object and its dynamics.

Therefore, the semiotic system can be given a new interpretation. Composite dynamic system: W = FSi, which determines the state in the information situation or information position, dynamic MFsi, which defines the rules for the transition of the control object from one information position to another.

rules MFsi = (τ, ρ, α, π), those that change the state of the formal system (control object) are connected by the dependence existing in the elements of the semiotic triangle (Frege's triangle). This means that the application of one of the four rules leads to the application of the remaining rules.

These dependencies are complex, their analytical representation is absent, and this is difficult and is the subject of research in semiotic systems of artificial intelligence. Therefore, it is easier to use the information approach and information modeling.

Extensions of formal control systems in the form of dynamic components MFsi provide the properties of systems openness. They create the ability to adapt the control object to management influences and changing external conditions.

This, in particular, makes it possible to significantly expand the possibilities of decision-making support in conditions of uncertainty, incompleteness and inconsistency of the initial information.

Types of uncertainties in the implementation of intelligent control

Traditional control methods, including some types of intelligent control, are based on the assumption that the state and control models of an object accurately describe its behavior. Methods based on this assumption are included in the classical control theory. However, in conditions of increasing volumes, growth of unstructured information and the impact of the external environment, deviations from this condition are characteristic.

Almost any model is a simplified description of a real object, its state and its behavior. The degree of simplification can be tolerable or create ambiguity. In the dynamics of the behavior of the control object, some characteristics of the object can change significantly in the course of its functioning. All this creates ambiguities in various models for describing an object and makes it difficult to manage it, including intellectual. The typical control model underlying the control algorithm or a set of established control rules is called nominal.

In conditions of significant uncertainty, the classical methods of control theory turn out to be inapplicable or give unsatisfactory results. In these cases, it is necessary to use special methods of analysis and synthesis of control systems for objects with indefinite models. The first step is to assess the type and value of the uncertainty.

The main types of uncertainties in management models are distinguished: parametric, functional, structural and signal.

Parametric uncertainty means that the constant parameters of the model are unknown or imprecise. For example, interval values ​​are used instead of dot values. In the transition to information measuring systems, we can talk about the lack of informational certainty of the parameters. Therefore, in many cases, the actual values ​​of the parameters may differ significantly from the accepted nominal values.

Signal uncertainty means that the control action or information flows in the control system are affected by interference that significantly changes the nominal signals. Such signals that deviate the control process from the nominal are called disturbances or noises. The difference is that the interference is passive and only changes the signal-to-noise ratio. Perturbation changes the signal with the same interference.

Modern intelligent control systems must ensure the autonomous operation of many related technical objects. This gives reason to talk about an intelligent control system (IMS). An intelligent system must solve complex problems, including planning, goal setting, forecasting, and so on. For versatility, adaptation and accuracy of solutions, it is advisable to use multipurpose intelligent control.

The multilevel architecture of an intelligent control system consists of three levels: conceptual, informational and operational (Fig. 1). A system based on such an architecture controls the behavior of complex technical objects in conditions of autonomous and collective interaction. The conceptual level is responsible for the implementation of higher intellectual functions.

Fig. 1. Multilevel intelligent control.

At the conceptual level, a semiotic (sign) representation of knowledge is used and messages are exchanged with the rest of the levels. The information and operational levels contain modules that support various intellectual and information procedures and transform them into management.

The main task of management at the conceptual level is the storage, acquisition and use of conceptual knowledge presented in a semiotic (symbolic) form.

Composite dynamic system: W = includes two components: static FSi which defines the dynamic sign system MFsi, which defines the system of rules (Fig. 1).

The acquisition of knowledge is based on a model of a real situation in the external environment. The highest intellectual functions include the functions of setting the main goal and subgoals, planning behavior and distributing impacts in a general plan of action.

At the information management level, the tasks of information modeling are solved, the main of which are: building an information situation, information position, which correspond to the component FSi... At the information management level, the tasks of building an information structure are solved, which is a reflection of the system of rules of the conceptual level and corresponds to the component Mfsi. The language environment of semiotic control at the information level is implemented by using various information units. Which serve as the basis for building an information situation, information position and information structure.

At the operational (executive) level, the implementation of management decisions (management influences) takes place. Management influences without fail change the informational position of the controlled object. Management influences can change, if necessary, the information situation of the controlled object. At the same time, there is usually no need to change the information situation. The main task of this level is to change the state and position of the control object and to report changes to the conceptual level.

The layered architecture has a number of features. It includes a number of human cognitive functions. It relies on the use of an informational approach to intelligent management.

The difference between intelligent and information technology should be noted. Information technology performs functions of supporting intelligent control. The main role is played by intelligent decision-making technologies. They make it possible, along with a solution or in the course of obtaining a solution, to search for new knowledge and the accumulation of intellectual resources. Information technologies create only information resources. This means that knowledge formalized in an explicit form, once mastered, can become part of the experience and part of the knowledge base and be used by it for solving problems and making decisions.

Conclusion

Intelligent control is effective and necessary when managing complex objects for which it is difficult or impossible to find formal models of functioning. The basis of intelligent control is semiotic models in the first place and informational ones in the second place. Intelligent control methods are diverse and applicable to technical, cognitive and transportation systems. Intelligent control is widely used for multi-purpose control. Modern intelligent management is being integrated into cloud platforms and services. When managing distributed organizations and corporations, it becomes necessary to take into account spatial relationships and spatial knowledge. Another problem is the limited number of intelligent technologies for working with tacit knowledge. Technically, the problem of knowledge management is associated with the transformation of information resources into intellectual resources and their application in intelligent technologies.

Bibliographic list

UDC 004.896

I. A. Shcherbatov

INTELLIGENT CONTROL OF ROBOTIC SYSTEMS UNDER UNCERTAINTY

Introduction

Intelligent control - the application of artificial intelligence methods to control objects of various physical nature. In the field of control of robotic systems, artificial intelligence methods are most widely used. This is due, first of all, to the autonomy of robots and the need for them to solve non-formalized creative tasks in conditions of incomplete information and various types of uncertainty.

Until recently, the specified class of problems remained the prerogative of natural intelligence: an operator of a control object, an engineer, a scientist, i.e., a person. Modern advances in the field of automatic control theory, intelligent methods of formalizing semi-structured tasks and managing complex technical systems make it possible to implement very complex robotic systems, which include mobile robotic platforms, flexible automated lines and android robots.

Robotic systems operate under conditions of incomplete input information, when the fundamental impossibility of measuring a number of parameters imposes significant restrictions on the control program. This leads to the need to develop a base of algorithms that allow, on the basis of indirect signs and measurable indicators, to calculate unmeasured parameters.

The uncertainty of the external environment in which the robotic system functions makes it necessary to include in the control system various kinds of compensators, modules for adaptation, accumulation and ranking of information.

Formulation of the problem

The aim of the research was the formation of approaches to the construction of intelligent control systems for robotic systems that are invariant with respect to the specifics of functioning, taking into account the incompleteness of input information and various types of uncertainty.

To achieve this goal, it is required to solve a number of interrelated tasks: to analyze the architectures of intelligent control systems for robotic systems; develop a generalized algorithm for situational identification of a robotic system; to develop a generalized diagram of a robotic system control system; to develop intelligent control systems for a manipulation robot, a mobile robotic platform and a flexible automated line.

Research methods

In the course of the research, the methods of the general theory of automatic control, the theory of fuzzy sets, neural networks, system analysis, and the theory of expert assessments were used.

Location of the robotic system in the external environment

For the implementation of intelligent control algorithms, the priority is the task of the current identification of the situation in which the robotic system is located. To solve this problem, a structural diagram of the situational identification system has been developed (Fig. 1).

The unit of technical vision and sensory sense is designed to determine changes in the state of the external environment and present a sensor map of the environment for further processing. The sensory map of the environment is an image of the situation in which the robot is at the current moment in time. The time interval for building a sensor map is selected based on the specifics of the subject area.

Knowledge base

Operator

Intellectual

interface

Identifier

algorithms

Organs of technical vision and sensory perception

External environment

Executive

mechanisms

Rice. 1. Block diagram of the situational identification system

Working memory, by analogy with expert systems, is designed to process information coming from sensors and processed using the existing algorithm base and the knowledge base (KB) of the robotic system.

The base of algorithms includes algorithms for preprocessing a sensor map (digital signal processing, recognition of sound images and images), calculating unmeasured parameters (functional dependences on measured parameters), restoring the completeness of information (checking knowledge for completeness and inconsistency, adapting knowledge taking into account nonstationarity and variable external conditions), mathematical operations, etc.

The knowledge base is a complex hierarchical structure containing a priori information about the external environment, laid down at the training stage, complete and consistent knowledge acquired by the robot in the process of functioning and perception of the external environment. Knowledge in the knowledge base is ranked according to the criteria of relevance and is updated taking into account changes in the specifics of the robot's functioning based on knowledge adaptation algorithms.

The most important block is the situation identifier. It is this block that is responsible for the correct recognition of the image of the situation based on the sensor map. The result information of this block is decisive for the selection of the robotic system control program.

And finally, an intelligent interface, which is required for communication with the operator. The operator controls the functioning of the robotic system, as well as monitoring the process to achieve the set goals. As a rule, the communication between the robot and the operator should take place using a natural language interface in a limited subset of the natural language.

The structure of a control system for a robotic system under conditions of uncertainty

The implementation of algorithms and programs for intelligent control of robotic systems in conditions of uncertainty is associated with a number of significant difficulties.

The complexity of the algorithms for preliminary processing of input information and the structural uncertainty of the behavior model of the robotic system itself determine the redundancy of the structure of the intelligent control system.

To solve the problem of controlling a robot under conditions of uncertainty, the following architecture of an intelligent control system has been designed (Fig. 2).

A situational identification system (SID) should be part of any intelligent control system for a robotic system. An intelligent control device (IUU) contains a BZ and a control program selection unit (BVPU). The purpose of this block is to develop a control action for the system of electric drives (ED) acting on the mechanical system (MS) of the robot.

Rice. 2. Block diagram of the intelligent control system of the robotic system

Industrial manipulator control systems

Traditional industrial manipulator control systems are divided into several classes. The first class of systems is programmed control systems.

The system of continuous control of the working body of the manipulator implies adjusting the manipulator to the reference model. This control algorithm does not take into account the losses in the manipulator MS and it is assumed that all the efforts developed by the drives are transferred to the working body.

The programmed force control system in the working body is used to control not only the force vector, but also the vector of the working body position. The system of independent control of movement and force in the working body of the manipulator for different degrees of mobility has two control loops with feedback: position and force.

In the system of coupled control of displacement and force in the working body of the manipulator, the task by the vector of the position of the working body is corrected by the current value of the force vector. This means that when the working body moves, the magnitude of its stroke is corrected by the force of the impact on the external environment.

Adaptive control systems are used when performing: operations of taking an arbitrarily located or moving object, arc welding of seams with a variable position, bypassing moving and unforeseen obstacles. For this purpose, adaptive systems with associative memory are used.

To control industrial manipulators, robust control systems are also used, which are currently widely used in practice.

Intelligent control implementation

The problem of the functioning of a robotic system under conditions of uncertainty is multifaceted.

Consider the problem of planning the behavior of a robotic system under conditions of uncertainty. To solve it, it is most expedient to use the technology of dynamic expert systems. The knowledge base of such an expert system is adjusted over time. If a production rule base is applied, then the composition of the production rules is continuously examined for completeness and consistency. In addition, due to adaptation algorithms, outdated and outdated rules are being updated and replaced. At the same time, special attention is paid to the issues of teaching the expert system without a teacher (self-learning), since monitoring the system of a highly qualified specialist is economically inexpedient.

The self-learning or self-tuning block of the knowledge base of the expert system requires careful study at the design stage of an intelligent control system for a robotic system.

my. It is on the quality of this stage of design work that the effectiveness of solving the task often depends. It should include subsystems for assessing the completeness and inconsistency of knowledge, assessing the quality of management and correcting knowledge.

Chronologically, the next stage after planning behavior can be the problem of issuing control commands to a robotic system in natural language. To create a natural language interface, in our opinion, the most appropriate implementation tool is the theory of fuzzy sets.

With the help of linguistic variables containing a certain, previously described term-set, a description of the subject area, a limited system of commands and objects that affect the robotic system and change under its action is made. The methods of fuzzification and defuzzification used in this case, as well as algorithms of fuzzy inference, have a significant impact on the accuracy of working out control actions and the speed of the robotic system.

And finally, the use of neural network control systems for robotic systems. The main advantage of a neural network is that there is no need to know or create a mathematical model of an object, since a neural network is a universal fuzzy approximator.

The object (robotic system) acts as a "black box". The neural network can act as a reference model for a controlled robotic system. It should be noted that this should be a learning multilayer neural network (object identifier). The neural network model is tuned to the control object by the mismatch between the output signals of the object and the model. It also forms a training sample for adjusting and adjusting the control device in accordance with the selected quality criterion.

Conclusion

The analysis made it possible to synthesize the architecture of an intelligent control system for robotic systems that is invariant with respect to the specifics of functioning. The developed situational identification algorithm makes it possible to build highly informative sensor maps of the external environment. The main approaches to the formation of intelligent control systems for robotic systems are described. The directions of the perspective development of the most effective methods of artificial intelligence used for the implementation of control devices are shown.

BIBLIOGRAPHY

1. Yurevich EI Fundamentals of robotics. - SPb .: BHV-Petersburg, 2007 .-- 416 p.

2. Manipulation systems of robots / ed. A.I. Korendyaseva. - M .: Mashinostroenie, 1989 .-- 472 p.

3. Burdakov SF Synthesis of robust regulators with elastic elements: collection of articles. scientific. tr. - No. 443. Mechanics and management processes. - SPb .: SPbSTU, 1992.

4. Protalinsky OM Application of artificial intelligence methods in the automation of technological processes: monograph. - Astrakhan: Publishing house of ASTU, 2004 .-- 184 p.

The article was received at editors 13.01.2010

INTELLECTUAL MANAGEMENT OF ROBOTICS SYSTEMS IN THE CONDITIONS OF UNCERTAINTY

I. A. Shcherbatov

The purpose of the given work is a formation of approaches to construction of intellectual control systems of robotics systems, invariant in relation to specificity of the functioning, considering incompleteness of the entrance information and various kinds of uncertainty. The analysis, allowed to synthesise architecture of an intellectual control system of robotics systems invariant in relation to specificity of functioning is carried out. The developed algorithm of situational identification allows to build up good touch cards of the environment. The basic approaches to formation of intellectual control systems of robotics systems are described. Directions of perspective development of the most effective methods of the artificial intellect applied to realization of actuation devices are shown.

Key words: robotics system, the robot, intellectual management, structural uncertainty, incompleteness of the information, touch card, neural network, the theory of the indistinct sets, self-trained expert system.

TOPIC 13. INTELLIGENT CONTROL SYSTEMS

A new generation of systems - intelligent systems (IS) - brought to life other principles of organizing the components of systems, new concepts, terms, blocks appeared that were not previously encountered in developments and, therefore, in scientific literature.

Intelligent systems are able to synthesize a goal, make a decision for action, provide an action to achieve the goal, predict the values ​​of the action result parameters and compare them with real ones, forming feedback, adjust the goal or control

Figure 13.1 shows a block diagram of the IS, where two large blocks of the system are highlighted: the synthesis of the goal and its implementation.

In the first block, based on the active assessment of the information received from the sensor system, in the presence of motivation and knowledge, a goal is synthesized and a decision is made for action. Active assessment of information is carried out under the influence of trigger signals. The variability of the environment and the system's own state can lead to a need for something (motivation), and if there is knowledge, a goal can be synthesized.

The goal is understood as an ideal, mental anticipation of the result of an activity. Continuing to actively evaluate information about the environment and the own state of the system, including the control object, when comparing the options for achieving the goal, you can make a decision for action.

Further, in the second block, a dynamic expert system (DES), based on current information about the environment and its own state of the IS, in the presence of a goal and knowledge, carries out an expert assessment, makes a decision on management, predicts the results of an action and develops control.

The encoded control is converted into a physical signal and fed to the actuators.

The control object, receiving a signal from the actuators, carries out one or another action, the results of which, presented in the form of parameters, through the feedback loop 2 enter the DES, where they are compared with the predicted ones. At the same time, the parameters of the result of the action, interpreted in accordance with the properties of the goal and entering block I, can be used for an emotional assessment of the achieved result: for example, the goal is achieved, but the result is not pleasant.

If the goal is achieved in all respects, then management is reinforced. Otherwise, the control is corrected. When the goal is unattainable, then the goal is adjusted.

It should be noted that with sudden changes in the state of the environment, or the control object, or the system as a whole, it is possible to synthesize a new goal and organize its achievement.

The IS structure, along with new elements, contains traditional elements and connections, the central place in it is occupied by a dynamic expert system.

Block 1 - goal synthesis Block II - goal realization

Figure 13.1 - IC block diagram

Formally, IS is described by the following six expressions:

T X S M T ;

T M S ST ;

C T S R T;

T NS= (A T) X T + (B T) U T;

T Y = (D T) X T;

T R Y WITH T ,

where T is a set of points in time;

X, S, M, C, R and Y - the set of states of the system, environment, motivation, goal, predicted and real result;

А, В and D - matrices of parameters;

Intelligent transformation operators using knowledge.

This description combines the representation of system objects in the form of a set of meanings, or a set of statements, or some other forms.

The dynamic properties of the IS can be described in the state space. Intelligent operators that implement perception, representation, concept formation, judgment and inference in the process of cognition are a formal means of processing information and knowledge, as well as making a decision. All these aspects should form the basis for constructing DES operating in real time and in the real world.

A dynamic expert system is some kind of complex education capable of assessing the state of the system and the environment, comparing the parameters of the desired and real results of an action, making a decision and developing control that contributes to the achievement of the goal. To do this, DES must have a stock of knowledge and have methods for solving problems. The knowledge transferred to the expert system can be divided into three categories:

1) conceptual (at the level of concepts) knowledge is knowledge embodied in the words of human speech or, more specifically, in scientific and technical terms and, naturally, in the classes and properties of environmental objects behind these terms. This also includes connections, relationships and dependencies between concepts and their properties, and the connections are abstract, also expressed in words and terms. Conceptual knowledge is the sphere, mainly of the fundamental sciences, if we take into account that the concept is the highest product of the highest product of matter - the brain;

2) factual, subject knowledge is a collection of information about the qualitative and quantitative characteristics of specific objects. It is with this category of knowledge that the terms "information" and "data" are associated, although such use of these terms somewhat diminishes their meaning. Any knowledge carries information and can be presented in the form of data; factual knowledge is what computers have always dealt with and with what they have been dealing most so far. The modern form of data accumulation is usually called databases. Of course, to organize databases, to find the necessary information in them, one must rely on conceptual knowledge;

3) algorithmic, procedural knowledge - this is what is usually called the words "skill", "technology", etc. In computing, algorithmic knowledge is implemented in the form of algorithms, programs and subroutines, but not any, but those that can be transmitted from hands into the hands and used without the participation of the authors. This implementation of algorithmic knowledge is called a software product. The most common forms of a software product are software packages, software systems, and others, focused on a specific area of ​​DES application. The organization and use of application packages is based on conceptual knowledge.

It is clear that conceptual knowledge is a higher, defining category of knowledge, although from a practical point of view, other categories may seem more important.

This is probably why conceptual knowledge is rarely embodied in a form that can be processed on computers. And if it is embodied, then most often it is incomplete and one-sided. In most cases, a person remains the bearer of conceptual knowledge. This slows down the automation of many processes.

Representations of conceptual knowledge, or rather, systems that implement all three categories of knowledge, but highlight conceptual knowledge in the foreground and work on the basis of its intensive use, are called knowledge bases.

The creation and widespread use of knowledge bases in IP is one of the most urgent tasks. The conceptual part of the knowledge base will be called the domain model, the algorithmic part - the software system, and the factual part - the database.

The next function of DES is problem solving. A problem can be solved by a machine only if it is formally posed - if a formal specification has been written for it. The latter should be based on some knowledge base. The domain model describes the general setting in which the task arose, and the specification describes the content of the task. Taken together, they make it possible to establish what abstract connections and dependencies, in what combinations and in what sequence should be used to solve the problem.

Application programs represent the specific tools behind these dependencies, and also contain algorithms for solving the equations that arise. Finally, the database supplies all or part of the initial data to execute these algorithms, the missing data must be contained in the specification.

These three parts of the knowledge base correspond to three stages of solving the problem:

1) construction of an abstract solution program (including the emergence of the problem, its formulation and specification);

2) translation of the problem into a suitable machine language;

3) broadcast and execution of the program.

The construction of an abstract program is associated with the representation and processing of conceptual knowledge in IS and, by definition, is the property of artificial intelligence.

Artificial intelligence is associated with the processing of texts, oral messages in natural language, with the analysis and processing of information (recognition of all types of images, theorem proving, logical inference, etc.).

The functions of DES are also the assessment of the results of solving the problem, the formation of the parameters of the future result of the action, the decision-making on management, the development of control and comparison of the parameters of the desired and real results. It provides for the modeling of processes to assess the possible consequences and the correctness of the solution to the problem.

Note that in real cases there is a problem of describing the objects under study. It is inappropriate to consider such a description a part of the task specification, since, as a rule, many tasks are posed relative to one object, which, of course, must be taken into account when forming the knowledge base. In addition, it may turn out that the problem that has arisen cannot be solved automatically to the end, for example, due to the incompleteness of the specification or description of the object.

Therefore, in the IS, it is advisable at certain stages to have an interactive mode of operation with DES. It should be remembered that the domain model describes the general environment (knowledge), and the specification describes the content of the task. Very important problems are the creation of a unified software environment and the synthesis of algorithms directly according to the formulation of the problem.

Depending on the goal facing the IS, the knowledge base, algorithms for solving a problem, making a decision, developing control can, of course, have a different representation, which, in turn, depends on the nature of solving problems. Accordingly, three types of DES can be seen. The structure of a DES of the first type is shown in Figure 13.2.

Figure 13.2 - Structure of DES of the first type

It is assumed here that conceptual and factual knowledge accurately reflects the processes and information related to a certain subject area.

Then the solution to the problem arising in this area will be obtained on the basis of rigorous mathematical methods, in accordance with the formulation and specification. The results of the decision study and the forecast are used to obtain an expert opinion and make a decision on the need for management. Then, based on a suitable control algorithm available in the knowledge base, a control action is formed.

The effectiveness and consistency of this impact, before it arrives at the control object, is assessed using a mathematical simulation model. Evaluation should be performed faster than real processes in IS.

However, DES that implement decision-making are complex software systems designed for automatic decision-making or to help decision-makers, and in the operational management of complex systems and processes, as a rule, they work under severe time constraints.

Unlike DES of the first type, designed to find the optimal solution and based on rigorous mathematical methods and optimization models, DES of the second type are mainly focused on solving difficult formalized problems in the absence of complete and reliable information (Fig. 13.3). It uses expert models based on the knowledge of experts - specialists in this problem area, and heuristic methods for finding a solution.

One of the main problems in the design of a DES of the second type is the choice of a formal apparatus for describing decision-making processes and building on its basis a decision-making model that is adequate to the problem area (semantically correct). Production systems are usually used as such an apparatus. However, the main research is carried out in the context of an algorithmic (deterministic) interpretation of a production system with its inherent sequential solution search scheme.

The resulting models are often inadequate to real problem areas characterized by the non-determinism of the process of finding a solution. The way out of this situation is search parallelism.

In reality, one should focus on combining DES of the first and second types into a computational-logical DES of the third type, where the knowledge base combines description in the form of strict mathematical formulas with information from experts, as well as, accordingly, mathematical methods for finding a solution with non-strict heuristic methods, and the weight of one or the other the component is determined by the possibility of an adequate description of the subject area and the method of finding a solution (Fig. 13.4).

Figure 13.3 - Structure of the second level diesel power plant

When developing a DES, the following problems arise:

1.determination of the composition of the knowledge base and its formation;

2. development of new and use of well-known theories and methods for describing information processes in IS;

3. development of ways to represent and organize the use of knowledge;

4. development of algorithms and software with parallelization and the use of "flexible logic";

1. finding suitable computing environments for the implementation of parallel algorithms in the formation of DES.

Figure 13.4 - The structure of the third level diesel power plant

Along with the above, it is important to note that DES should have the property of adapting to a dynamic problem area, the ability to introduce new elements and connections into the description of situations, change the rules and strategies for the functioning of objects in the process of making a decision and developing control, working with incomplete, fuzzy and contradictory information and etc.

Dynamic expert systems operate as part of ISs with feedback, and therefore it is important to ensure the stable operation of such ISs.

From the traditional point of view, it can be assumed that the duration of the DES response to input influences, i.e. the time spent on processing input information and developing a control action is pure delay. Based on the frequency analysis, it is possible to estimate the change in the phase properties of the system and thereby determine the stability margin. If necessary, you can correct the system using filters.

However, from the point of view of the classical control theory, ISs are multi-object multi-connected systems, the analysis of the stability of which by conventional methods is very difficult.

At present, the theory of robust control (-control theory, -control) is one of the intensively developing branches of control theory. Relatively young (the first works appeared in the early 80s), it arose from the urgent practical problems of the synthesis of multidimensional linear control systems operating under conditions of various kinds of disturbances and changes in parameters.

You can approach the problem of designing control of a real complex object operating under uncertainty in a different way: do not try to use one type of control - adaptive or robust. Obviously, one should choose the type that corresponds to the state of the environment and the system, as determined by the information available to the system. If, in the course of the system's functioning, it is possible to organize the receipt of information, it is advisable to use it in the control process.

But the implementation of such a combined control until recently ran into insurmountable difficulties in determining the algorithm for choosing the type of control. The advances achieved in the development of artificial intelligence problems make it possible to synthesize such an algorithm.

Indeed, let us set the task: to design a system that uses adaptive and robust control and selects the type of control based on artificial intelligence methods. For this, we will consider the features of both types and, taking into account their specific qualities, we will determine how a combined control system can be built.

One of the basic concepts in the theory of robust control is the concept of uncertainty. The uncertainty of the object reflects the inaccuracy of the object model, both parametric and structural.

Let us consider in more detail the forms of specifying uncertainty in a robust control theory using a simple system - with one input and one output (Figure 13.5).

Signals have the following interpretation: r - setting input signal; u - input signal (input) of the object; d - external disturbance; y is the output signal (output) of the object being measured.

Figure 13.5 - System with one input and one output

In control theory, it is convenient to set the uncertainty in the frequency domain. Suppose that the transfer function of a normal plant P, and consider a disturbed plant, the transfer function of which,

where W is a fixed transfer function (weight function);

- an arbitrary stable transfer function that satisfies the inequality.

This indignation will be called admissible. Below are some variants of uncertainty models:

Appropriate assumptions must be made for the quantities and W in each case.

The uncertainty of the input signals d reflects the different nature of external disturbances acting on the plant and the controller. An indefinite object, thus, can be considered as a kind of set of objects.

Let's choose some characteristic of systems with feedback, for example, stability. Regulator C is robust with respect to this characteristic if any of the set of objects defined by uncertainty possesses it.

Thus, the concept of robustness implies the presence of a controller, a set of objects, and the fixation of a certain characteristic of the system.

In this work, we will not touch upon the entire set of problems solved within the framework of control theory. Let us touch only on the problem of minimum sensitivity: constructing a controller C that stabilizes the closed-loop system and minimizes the influence of external disturbances on the output y, in other words, minimizes the norm of the matrix of transfer functions from external disturbances to the output y.

One of the features of the solution of this, and indeed of the entire set of robust control problems, is the fact that in advance, in the process of designing the controller, we impose restrictions on the input actions and the uncertainty of the object in the form of inequalities.

During the operation of a robust system, information about uncertainties in the system is not used for control.

Naturally, this leads to the fact that robust systems are conservative and the quality of transient processes sometimes does not satisfy the developers of these systems.

Similar to a robust adaptive control system, an adaptive control system is constructed for objects, information about which or about the effects on which is not available at the beginning of the system's functioning. Most often, the adaptation property is achieved through the formation, in an explicit or implicit form, of a mathematical model of an object or an input action.

This distinguishes both search adaptive control, which is based on the search and retention of the extremum of the control quality indicator, and non-search control, which is based on compensation for the deviation of the actual changes in the controlled coordinates from the desired changes corresponding to the required level of the quality indicator. Further, according to the refined model, the adaptive controller is adjusted.

Thus, the main feature of adaptive control systems is the ability to obtain information in the process of functioning and use this information for control.

Moreover, in adaptive systems, a priori information about the uncertainty in the system is always used. This is the fundamental difference between the adaptive and robust approach.

Consider a simple adaptive control system that monitors the input signal in the presence of interference at the input of the object (Figure 13.6).

Drawing. 13.6 – Adaptive control system

The formal difference from the circuit in Figure 13.5 is the adaptation block A, which, based on the output signal of the object and the signal characterizing the given quality, generates a signal for adjusting the coefficients of the adaptive controller.

Bearing in mind the disadvantages of each of the regulators, it is advisable to try to use their advantages by proposing a combined control scheme for the object. The adaptive system with the help of the adaptation unit generates some information about the state of the external environment. In particular, in the case under consideration, one can obtain information about the external disturbance d. The control algorithm С а corresponds to the current state of the external environment, according to the criterion laid down in the adaptation block. But the adaptive system requires the input signal r to have a sufficiently wide frequency range, and imposes severe restrictions on the value and frequency spectrum of the external disturbance signal d. Therefore, adaptive systems can operate only in narrow ranges of the input signal r and external disturbance d. Outside these ranges, the adaptive system has poor control quality and may even become unstable.

The properties of robust and adaptive control considered above lead to the conclusion that in the process of system functioning, in some cases it is advantageous to use robust control, in others - adaptive control, i.e. be able to combine control depending on the state of the external environment.

Combined control. The main question in the design of combined control systems is how, on the basis of what knowledge (information), to select one or another type of control.

The widest possibilities for this are presented by artificial intelligence methods. Their advantage over simple switching algorithms is the use of a wide range of data and knowledge to form an algorithm for choosing a control type.

If we formally combine the circuits shown in Figures 13.5, 13.6, we get a combined control circuit (Figure 13.7).

As can be seen from the figure, the control signal should switch from a robust controller to an adaptive one and vice versa - as the environment changes during the system operation. Using the methods of the theory of intelligent systems, it is possible to provide a transition from one type of control to another, depending on the operating conditions of the system.

Figure 13.6 - Combined control scheme

Let us first consider what information can be used to operate the intelligent unit of the system. As you know, systems with one input and one output are well described in the frequency domain. Therefore, it is natural to use frequency characteristics to organize the decision-making process when choosing the type of control.

As mentioned above, the frequency response of a robustly controlled system corresponds to the worst combination of parameters in the uncertainty region. Therefore, robust control can be taken as one of the boundaries of the selected control.

Another boundary is determined by the capabilities of the system under study (drive speed, power-to-weight ratio, etc.). Between these two boundaries is an area where it makes sense to use adaptive control.

Figure 13.7 - Combined control scheme

Since the adaptive algorithm is sensitive to the initial stage of the system's functioning, at this stage it is advisable to use robust control, which is sufficiently insensitive to the rate of change of the external noise. But its disadvantage is the long duration of the transient processes and the large permissible values ​​of the output coordinate under the action of interference.

After some time, it makes sense to switch the robust control to adaptive.

Adaptive control allows you to more accurately track the input signal in the presence of information about the interference. Adaptive control is demanding on the richness of the input signal spectrum, and, for example, with slowly varying signals, adaptation processes can be disrupted or severely slowed down. In such a situation, it is necessary to switch to robust control again, which guarantees the stability of the system.

It follows from the above that for the system to function, it is necessary to have information about the frequency spectrum of the useful interference signal and about the signal-to-noise ratio.

In addition, preliminary information is required on the frequency spectrum on which the adaptive system operates and on the particular characteristics of the control object at the boundaries of the uncertainty region. From this information, it is possible to form a database into which information, individual for each class of objects, is entered in advance. Information about the frequency spectrum of the useful signal, interference and signal-to-noise ratio is entered into the database as the system operates and is constantly updated.

The content of the database can be used in the knowledge base, which is formed in the form of rules. Depending on the specific properties of the system, switching of two types of control can be set. The required rules are formed in one of the logical systems suitable for the case under consideration.

Having databases and knowledge, it is possible to develop a decision-making mechanism that will ensure the correct choice of the type of control, depending on the conditions of the system's functioning.

Figure 13.8 - Block diagram of a system with an intelligent unit (IS)

The intellectual part of the system works discretely, at specified time intervals. Figure 13.8 shows a block diagram of a system with an intelligent IS unit that provides a choice of the type of control.

The input of the block receives the signal r and the measured one, the output signal of the object y. In the block of preliminary processing of information BPOI, according to the time characteristics of signals r (t), y (t), the frequency characteristics of the input signal r (w) and external disturbance d (w), the relative position of the spectra r (w) and d (w) and the characteristic values ​​of the signal-to-noise ratio r (w) / d (w). All this information goes to the DB database. The decision-making block of the BPR, using the generated knowledge base of the knowledge base and the database data, develops a decision in accordance with which one of the control types is switched on. At the next interval, the process is repeated using new data.

INTRODUCTION

The operating conditions of modern technological complexes lead to the need for accounting in the process of monitoring and control. the following types of uncertainty:

1. Low accuracy of operational information received from control objects, arising due to the large error of sensors for measuring technological parameters (flow rate, pressure, etc.), their low reliability, communication channel failures, a large delay in the transmission of information over control levels, the inability to measure parameters at all points of the technological process required for the models.

2. Inaccuracy of models of objects of control and management caused by: non-equivalence of solutions of system multilevel hierarchical models and individual local problems used in practice; incorrect decomposition of the general control problem, excessive idealization of the technological process model, rupture of essential links in the technological complex, linearization, discretization, replacement of the actual characteristics of the equipment with passport ones, violation of the assumptions made when deriving equations (stationarity, isothermality, homogeneity, etc.).

3. Fuzzy decision making in multi-level hierarchical systems, due to the fact that the presence of clear (precise) goals and coordinating decisions at each level of control and management, and for each local regulation device, complicates the coordination process and predetermines the long iterative nature of the coordination of decisions.

4. The presence of a human operator, including a dispatcher, in the control loop and conducting the coordination process in a real production system in natural language, leads to the need to take into account the difficulties of representing the knowledge of the dispatcher in the form of algorithms and the consistency of the solution obtained by the computer with its assessment.

“Excessive pursuit of accuracy began to have an effect that nullifies control theory and systems theory, since it leads to the fact that research in this area focuses on those and only those problems that lend themselves to exact solutions. Many classes of important problems, in which the data, goals and constraints are too complex or poorly defined to allow accurate mathematical analysis, have been and remain on the sidelines simply because they do not lend themselves to mathematical treatment. "

L.Zadeh

Among modern production processes, there are many that have a complex of qualities that are unexpected for the classical theory of automatic control (TAU). This "inconvenient" or, as they are also called, "Semi-structured" or "Ill-defined" objects have such properties as uniqueness, lack of a formalized purpose of existence and optimality, nonstationarity of structure and parameters, incompleteness or almost complete absence of a formal description of the object.

Conceptual framework

management under uncertainty

Uncertainties which are understood as sources of uncertainty, are rather conditionally subdivided into the following three large groups:

1. uncertainty and incompleteness of information about the situation, which is used to make a decision on the assessment of the quality of functioning or the formation of control over the functioning of the system - system and environment uncertainty factor;

2. factors generated by uncertainty, fuzziness of thinking and knowledge of a person- uncertainty that manifests itself in the interaction of a person with the system and his environment;

3. uncertainty factors, fuzziness(inaccuracy) accumulated knowledge, concentrated in the knowledge bases of artificial intelligent systems, uncertainty of operating this knowledge in the process of implementation certain logical and logical-algebraic procedures for collecting and processing information, developing, choosing and making managerial decisions.

Classification of factors (sources) of uncertainty that need to be taken into account in the study of complex systems is shown in Figure B.1.

Figure B.1. Classification of uncertainties

Methodology for analyzing and accounting for uncertainty factors in

management in complex organizational and technical systems ...

(ACS with DSS and DSS-decision support systems and decision-making systems)

1. Problems and generalized formalization of tasks for the development and

making management decisions in conditions of uncertainty….

2. Deterministic game approach to decision making under conditions

uncertainty ………… .. …………… .. …………………… ..

3. A stochastic approach to solving decision-making problems in

conditions of uncertainty… .. ………………………………………

4. Probabilistic - statistical approach to decision making in us-

in the face of uncertainty …………………………………………… ..

5. The probabilistic approach to decision making in conditions of uncertainty

laziness… .. ……………………………………………………………

6. Fuzzy - stochastic approach to decision making in conditions

uncertainties …………………………… .. ……………………… ..

7. Opportunity theory and the problem of decision making in conditions

uncertainties ……………………. …………………………………

8. Fuzzy - a possible approach to decision making in conditions

uncertainties ……………………………………………………….

9. The linguistic approach to decision making in conditions of uncertainty

divisions .. ……………………… .. ………………………………….

The control of semi-structured objects from the point of view of the classical TAU is a rather difficult, practically insoluble problem. This is due to the fact that when building a traditional automatic control system (ACS), it is necessary to first formally describe the control object and form control criteria on the basis of a mathematical apparatus operating in quantitative categories. If it is impossible to give an exact mathematical description of the object and the criteria for controlling it in quantitative terms, the traditional TAU turns out to be inapplicable.

For example, the classical ACS by deterministic and stochastic systems is successfully used to build ACS by aircraft, power plants, etc., but attempts to extend traditional methods to areas such as biosynthesis, multiphase chemical technological processes associated with roasting, melting, catalysis etc., did not give tangible practical results, despite the increasingly complicated mathematical methods of their description.

However, in practice, such semi-structured objects are quite successfully controlled by a human operator, who is rescued by the ability to observe, analyze and memorize information, draw certain conclusions, etc., and, as a result, make the right decisions in an environment of incomplete and fuzzy information. Thanks to his intellect, a person can operate not only with quantitative(which, to a certain extent, a machine can), but also with qualitative informal concepts, as a result of which it quite successfully copes with the uncertainty and complexity of the management process. Therefore, the construction of models of approximate reasoning of a person and their use in ACS is today one of the most important directions in the development of TAU.

There is no doubt that a significant increase in the efficiency of management of complex objects consists in the creation of intelligent ACS capable to one degree or another reproduce certain intellectual human actions associated with the acquisition, analysis, classification of knowledge in the subject area of ​​technological process control, as well as operating knowledge. accumulated by the human operator or by the system itself in the course of practical activities to control the object.

The need to work in these conditions makes it difficult to use standard automation systems and APCS... It is especially difficult to describe the areas of permissible operating modes of equipment in such conditions when the setting of strict (clear) restrictions for the process control system and automation systems lead to automatic or manual shutdown of these systems. Therefore, it is extremely important to use for the description and formalization of areas of permissible operating modes of equipment theories of artificial intelligence (AI) and intelligent systems (IS).

Due to the rapid development of computing technology in recent years the use of new methods of intelligent management in industry began... And although the first applications of intelligent ACS took place in Europe, such systems are most intensively introduced in Japan. Their range of applications is wide: from the control of industrial robots, rectification plants and blast furnaces to washing machines, vacuum cleaners and microwave ovens. At the same time, intelligent ACS can improve the quality of products while reducing resource and energy consumption and provide a higher resistance to the impact of disturbing factors in comparison with traditional ACS.

An intelligent system means(K.A. Pupkov) a set of technical means and software combined by an information process, working in conjunction with a person (a group of people) or autonomously, capable of synthesizing a goal on the basis of information and knowledge, with motivation, making a decision for action and finding rational ways to achieve goals.

The main architectural feature that distinguishes intelligent control systems (IMS) from "traditional"Is a mechanism for obtaining, storing and processing knowledge for the implementation of its functions.

The creation of intelligent control systems is based on two principles: situational control (control based on the analysis of external situations or events) and the use of modern information technologies for knowledge processing (expert systems, artificial neural networks, fuzzy logic, genetic algorithms, and a number of others).

Program No. 14 of fundamental research at OEMMPU RAS

"ANALYSIS AND OPTIMIZATION OF FUNCTIONING OF MULTI-LEVEL, INTELLIGENT AND NETWORK CONTROL SYSTEMS UNDER UNCERTAINTY"

1. Rationale for the Program

1.1. Scientific and practical significance

The intensive development of technology (network interaction, miniaturization of computers, increasing their speed, etc.) imposes new requirements on modern control systems and opens up new opportunities both at the level of embedded control systems (at the level of large dispatch centers) and at the network level (communication- network, group) interaction of decentralized multi-agent systems. Control systems are increasingly acquiring the character of information control systems and are studied at the intersection of control, computation and communication theories. So, taking into account the properties of communication channels (communication) is necessary, for example, in decentralized (multi-agent) systems, and the characteristics of the built-in computer are important when implementing in multi-level control systems such intellectual functions as technical vision, action planning, training, multi-criteria decision-making, reflection, etc. etc. In particular, the intellectualization of control is designed to increase the degree of autonomy of the functioning of systems, when the absence of quantitative models of dynamics or disturbances in the functioning of the control object, causing the loss of the adequacy of quantitative models (for example, equations describing the evolution of a complex system), enhance the role of qualitative ones (the so-called “ knowledge ”, for example, logical-linguistic) models of the object and environment used at the upper levels of the control system.

The program is aimed at solving fundamental problems arising in the priority areas of science, technology and technology of the Russian Federation. The task is to obtain new fundamental and applied results in the field of control theory for complex technical, human-machine and other systems, taking into account the uncertainty and lack of initial information, including: the theory of analysis and synthesis of stochastic systems, the theory of creating control systems for motion and technological processes, with current diagnostics and control over the technical condition, as well as the theory of creating automated design systems and intelligent control based on modern information technologies.

Due to the variety of use of control theory, analysis and optimization in various applications (transport, logistics, production, aviation and space systems, submarine and surface ships, etc.), it is necessary to take into account a large number of complexity factors, such as:

Multilevel management,

Decentralization,

Nonlinearity,

Multiconnection,

Distribution of parameters,

Different scales of processes in space and time,

High dimension,

Heterogeneity of the description of subsystems,

Multimode,

The presence of impulse influences,

Presence of coordinate-parametric, structural, regular and singular perturbations,

Use of deterministic and probabilistic models for describing the uncertainty of information about the state vector and system parameters, about the properties of measurement errors and the environment,

The presence of delay effects in control or object,

· General structural complexity of modern control systems.

To achieve this goal and solve the main tasks, the Program includes research and development in the following main areas:

1. Analysis and optimization of functioning in different time scales of multilevel control systems with incomplete information.

2. Management and optimization in multilevel and decentralized systems of an organizational and technical nature.

2.1. Management and optimization in network-centric systems.

2.2. Intelligent control of moving objects.

2.3. Modeling and optimization of multilevel real-time information and control systems.

Direction 1. Analysis and optimization of functioning in different time scales of multilevel control systems with incomplete information

The complexity of many modern control systems often does not allow obtaining in advance a complete description of the processes occurring within the system and its interaction with the environment. As a rule, real systems are described by nonlinear equations of dynamics and quite often the mathematical models of control systems take into account only the permissible ranges of changes in the parameters and characteristics of individual elements without specifying these parameters and characteristics themselves.

In addition, in some systems, in particular, micromechanical and quantum systems, the use of classical methods of description in continuous or discrete time is hampered by the fact that the arising internal and / or external forces of interaction, as well as control actions, are of a transient, impulsive nature and cannot be accurately calculated. ... The system seems to function in different time scales: real (slow) and fast (impulse). Such temporal variability of scales is an intrinsic property of many modern control systems, including systems with multilevel control, in which the upper levels use qualitative and discrete models, and the lower ones - more often quantitative models with continuous time.

For this reason, the development of methods for the mathematical formalization of the description of the functioning of such systems in hybrid (continuous-discrete) time, the study of their properties for controllability and stability under conditions of incomplete information, opposition and non-standard constraints on controls and phase variables is an urgent task. The development of methods for the synthesis of optimal control of such continuous-discrete systems, both deterministic and stochastic, is an equally urgent task.

In addition, in conditions of uncertainty and a shortage of a priori information, the tasks of optimizing the process of collecting and processing information (monitoring observations and optimal filtering) are very relevant.

Direction 2. Management and optimization in multilevel and decentralized systems of an organizational and technical nature

2.1. Management and optimization in network-centric systems

Modern complex organizational and technical systems are characterized by high dimensionality, decentralization, multilevel management, the need for effective planning of activities, taking into account training, multicriteria of decisions made and reflection of controlled subjects.

Problems of planning and control of discrete and continuous distributed multiply connected systems of large dimension are also characterized by different scales of processes not only in time, but also by distribution and different scales in space and represent one of the most complex and laborious classes of optimization problems. For this reason, it is advisable to develop research methods and approaches to finding accurate and approximate solutions, as well as simulation tools for use in decision support systems for planning, design and management of complex technical, organizational (including transport and logistics) and information systems.

To manage group interaction, the components of decentralized organizational and technical systems (network-centric systems, production systems, computing, telecommunication and other networks, etc.) in the context of restrictions on communication channels and the complexity of calculations are of great importance for the characteristics of information processing processes, as well as restrictions on decision-making time, computational capabilities and bandwidth of communication channels. Therefore, it is relevant to develop optimization methods (taking into account the listed restrictions) of the structure of complex organizational and technical systems, including with the simultaneous consideration of many criteria: the detail of the initial data, the efficiency of collecting information, planning and reflective decision-making, the limited performance of individual computers, reducing the duplication of work. , as well as the share of ancillary computations associated with data transmission services.

Multilevel and decentralized systems are characterized by distributed decision making in real time in conditions of information countermeasures, as well as incompleteness and heterogeneity of information, often of a multi-criteria qualitative and subjective nature. For this reason, it is necessary to develop methods for creating adequate information support systems and supporting the adoption of strategic and operational decisions in conditions of incomplete information and opposition. For this, it is advisable, in particular, to develop: multi-agent models of dynamic organizational and technical systems, including network models with conflicting agents, models of group behavior and its forecast, assessing the balance of interests and the formation of coalitions in these systems, as well as the development of information technologies and means of presenting information. about the external environment and knowledge of intelligent agents.

2.2. Intelligent control of moving objects

Quantitative models cannot always be created to solve the set tasks, therefore, along with traditional methods, the Program uses artificial intelligence methods. Artificial intelligence, as a field of knowledge, has undergone a huge leap over the past fifty years both in the development and refinement of the very concept of intelligence, and in the field of practical application of artificial intelligence in various fields of human activity: in technology, economics, business, medicine, education, etc. Many theoretical positions and methods of artificial intelligence have been transformed into applied intelligent technologies based on knowledge.

The peculiarity of the modern generation of intelligent systems is that they rely on a complex model of the external environment, which takes into account both quantitative information and qualitative models - knowledge about the possible behavior of various objects of the external environment and their interrelationships. The use of such models became possible due to the development of methods for representing knowledge, methods of integrating data from different sources, a significant increase in the speed and memory of computers.

The presence of a model of the external environment allows modern intelligent control systems for moving objects to make decisions in conditions of multi-criteria, uncertainty and risk, and the quality of these decisions can exceed the quality of decisions made by a person under conditions of information overload, limited time and stress.

In this regard, an urgent task is to develop new means and methods for the development of intelligent control of moving objects in the presence of the above factors.

2.3. Modeling and optimization of multilevel real-time information and control systems

The relevance of research in this direction is due to the need to develop methods for the analysis and synthesis of multilevel open modular real-time information and control systems (IMS RT) of multi-mode and multipurpose objects operating under conditions of uncertainty, structural disturbances and emergency situations (NSS). Among these objects of management are critical objects and systems of responsible use that determine the security of the state.

Obviously, the problems and tasks of creating systems of this class can be successfully solved on the basis of the development of a unified theory and applied software-oriented methods of dynamic and scenario analysis and synthesis of the structure of such systems, their algorithmic, software and information support, mechanisms for the development of effective management influences. These, first of all, include the development of a formalized methodology for the design of open information and control systems, including models and methods for synthesizing the modular structure of object-oriented I&C RT with an open architecture, which is optimal according to various efficiency criteria. On the basis of the results obtained at the stage of dynamic analysis, an optimal functional modular structure of data processing and control is synthesized, i.e., the optimal composition and number of I&C RV modules is determined, the system interface is synthesized, and the structure of its software and information support for processing input flows of applications is determined.

To plan actions and support decision-making in conditions of uncertainty, structural disturbances and emergency situations, it is advisable to use the methods of scenario analysis and synthesis of effective control actions in the IMS RV. In this case, a mathematical model of the propagation of structural disturbances and emergency situations will be formed in the language of weighted or functional sign graphs. On the basis of this model, rational scenarios for managing objects will be synthesized using the concepts of working capacity, resistance and survivability of their constituent elements. The synthesis of scenarios for eliminating the causes and consequences of NSS in multi-mode target objects will be carried out taking into account dynamically determined time and resource constraints. It is also necessary to develop formulations and methods for solving inverse problems of survivability control for multi-mode and multipurpose objects operating under conditions of uncertainty, structural disturbances and emergency situations.

The above-mentioned specificity of systems and objects of management, the scientific and practical significance of solving the problems of management, analysis and optimization for them make it possible to formulate the following main goals and objectives of the Program.

1.2. Main goals and objectives

The main goal of the Program is to solve the fundamental problems of control theory that hinder the implementation of promising projects of important state importance in the field of managing complex dynamic and intelligent systems with applications to control the movement of technical objects and processes in technological and organizational systems.

Research will be carried out on the following generalized topics.

Direction 1

· Development of methods for stabilization of nonlinear systems in situations of incomplete measurement of coordinates and restrictions on the permissible structure of control forces.

· Development of methods of robust and adaptive observation and control in conditions of deterministic, probabilistic and other models of uncertainty of the parameters of the control object and the functioning environment.

· Development of methods and algorithms for qualitative and quantitative analysis of continuous, discrete and multilevel continuous-discrete dynamic models and control synthesis based on the reduction method with vector and matrix comparison functions and model transformations.

· Investigation of the problem of optimal control of a new class of mechanical systems moving in resisting media due to changes in the configuration or movement of internal bodies.

· Development of methods of mathematical formalization and solution of problems of shock interaction of mechanical systems in the presence of dry friction.

· Development of methods for optimal control of discrete-continuous and impulse dynamical systems.

· Development of methods for guaranteed control of nonlinear objects exposed to uncontrolled disturbances in the form of dynamic games.

· Development of the theory of control of quantum systems.

· Development of methods and algorithms for the analysis of dynamic properties such as stability, invariance, dissipativity for assessing the state and synthesis of multi-level control of systems with a heterogeneous description of the dynamics of processes at different levels.

Direction 2.1

· Methods for solving problems of control of network-centric systems of large dimension with distributed parameters and processes of different scales (in space and time).

· Models and methods of communication-network decentralized intelligent management of distributed projects and programs.

· Methods for optimizing the structure of multilevel and decentralized systems.

· Methods and structures of computer implementation of network-centric control in a mathematically homogeneous space of distributed and parallel computing.

· Models and methods of group decision-making based on incomplete, heterogeneous, qualitative and subjective information.

· Models and methods of planning and managing complexes of interrelated operations in complex technical and transport and logistics systems.

· Development of principles, architecture, methods and algorithms for creating distributed software intelligent systems based on multi-agent technologies.

· Development of models and methods of information management in multi-agent network structures.

Direction2.2

· Development of generalized models of situational management, reflecting the features of inclusion in the structure of models of fuzzy, neural network and logical-dynamic elements.

· Development of a method for planning routes that provide the property of communication stability of a group of controlled dynamic objects, heterogeneous (quantitative and qualitative) in their model representation.

· Development of methods for analysis and synthesis of adaptive real-time modeling platforms, taking into account nonlinearity, multi-connectivity, high dimensionality of control objects with an application to marine mobile objects.

· Optimization of intelligent systems of multilevel control of moving objects in a conflict environment, taking into account their group interaction, multi-criteria, uncertainty and risk.

· Development of methods for providing technical vision for intelligent control systems.

· Development of methods for intelligent control of dynamic objects performing complex maneuvering, based on the organization of forced movement in the state space of the system.

Direction2.3

· Models and methods of analysis and optimization of the modular structure of object-oriented multi-level information management systems of real time with an open architecture in conditions of uncertainty and structural disturbances.

· Methods of analysis and optimization of modes of electric power systems and their control.

· Models and methods of the scenario-indicator approach to the search for points of vulnerability for management tasks.

· Methods for modeling, analysis and optimization of multi-mode control processes of moving objects.

· Development of methods and algorithms for intelligent identification of nonlinear non-stationary objects to improve control efficiency through the formation of a technological knowledge base based on a priori information about the control object.

· Geoinformation technologies for modeling natural and technogenic complexes in the tasks of managing ecosystems of megalopolises.

· Analysis and optimization of information support for navigation and control systems.

· Models and methods for managing production processes.

The results of the developed theory and methods of analysis and synthesis of control systems will be used in the following areas:

· traffic control in aviation and astronautics, land and sea objects, vehicles;

· multi-agent network-centric systems, production systems, computing, telecommunication and other networks ;

· transport and logistics systems ;

· Global energy, gas transmission and other large-scale infrastructure systems;

· Information support systems for management tasks and support for strategic and operational decisions under conditions of incomplete information and opposition.

Fundamental problems of the theory of constructing control systems require their intensive development. The development of research in this direction will allow:

Development of the theoretical foundations for solving the complex triune problem of control-computation-communication (the problem is " Control- Computation- Communication") for complex information and control systems, including in conditions of restrictions on communication channels and failures of subsystems;

To solve the problems of managing fundamentally new objects and processes related to moving objects, special-purpose objects, technological and organizational systems;

To create effective methods of functional diagnostics and ensuring the fault tolerance of control systems of aircraft and other moving objects, as well as the dynamic stability of electric power systems;

To improve the quality, speed up and reduce the cost of the development of design solutions through algorithmization and automation of the process of developing control systems.

Hereinafter, control is understood in a broad sense, including communication-network, group, distributed control (in the English-language literature - control in networks, control over networks, distributed control, etc.)