Advanced Control Systems : : Theory and Applications.

• Superior document: River Publishers Series in Automation, Control and Robotics Series
• Author: Kondratenko, Yuriy P.
• TeilnehmendeR: Kuntsevich, Vsevolod M.; Chikrii, Arkadii A.; Gubarev, Vyacheslav F.
• Place / Publishing House: Aalborg : : River Publishers,, 2021.; Ã2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Series: River Publishers Series in Automation, Control and Robotics Series
• Physical Description: 1 online resource (476 pages)

Advanced Control Systems: Theory and Applications provides an overview of advanced research lines in control systems as well as in design, development and implementation methodologies for perspective control systems and their components in different areas of industrial and special applications.

Table of Contents

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- I: Advances in Theoretical Research on Automatic Control -- 1: On Descriptor Control Impulsive Delay Systems that Arise in Lumped-Distributed Circuits -- 1.1 Introduction -- 1.2 Example of Descriptor Control System -- 1.3 Restrictions, Definitions, and States of System -- 1.4 A Nonlinear Circuit with Transmission Lines in the Presence of Pulse Perturbations -- 1.5 Conclusion -- 2: An Extremal Routing Problem with Constraints and Complicated Cost Functions -- 2.1 Introduction -- 2.2 General Notions and Designations -- 2.3 General Routing Problem and Its Specific Variant -- 2.4 Dynamic Programming, 1 -- 2.5 Dynamic Programming, 2 -- 2.6 Computational Experiment -- 2.7 Conclusion -- 2.8 Acknowledgment -- 3: Principle of Time Stretching for Motion Control in Condition of Conflict -- 3.1 Introduction -- 3.2 Equivalence of the Pursuit Game with Delay of Information to the Game with Complete Information -- 3.3 Principle of Time Stretching in Dynamic Games of Pursuit -- 3.4 Integro-Differential Game of Pursuit -- 3.5 Illustrative Example of the Integro-Differential Game of Pursuit -- 3.6 Soft Meeting of Mathematical Pendulums -- 3.7 Conclusion -- 4: Bio-Inspired Algorithms for Optimization of Fuzzy Control Systems: Comparative Analysis -- 4.1 Introduction -- 4.2 Related Works and Problem Statement -- 4.3 Bio-inspired Algorithms of Synthesis and Optimization of Rule Bases for Fuzzy Control Systems -- 4.3.1 ACO Algorithm for Synthesis and Optimization of Rule Bases for the Mamdani-Type FACS -- 4.3.2 Genetic Algorithm for Synthesis and Optimization of Rule Bases for the Mamdani-Type FACS.

4.3.3 Algorithm of Automatic Rule Base Synthesis for the Mamdani-Type FACS Based on Sequential Search -- 4.4 Development of the Rule Base of the Fuzzy ControlSystem for the Multipurpose Mobile Robot -- 4.5 Conclusion -- 5: Inverse Model Approach to Disturbance Rejection Problem -- 5.1 Introduction -- 5.2 Disturbance Rejection via Inverse Model Control -- 5.2.1 Inverse Model Control Principle -- 5.2.2 Inverse Model Design -- 5.2.3 Inverse Model Based Feedforward Control -- 5.2.4 Inverse Model Based Disturbance Observer -- 5.2.5 Disturbance Decoupling Compensator Design -- 5.3 Sliding Mode Inverse Model Control -- 5.3.1 Sliding Mode Equivalence Principle -- 5.3.2 Variable Structure Feedforward Compensator -- 5.3.3 Variable Structure Disturbance Observer -- 5.4 Discrete Inverse Model Control -- 5.4.1 Problem Statement -- 5.4.2 Discrete Disturbance Observer -- 5.4.3 Disturbance Observer Parameterization -- 5.4.4 Disturbance Compensator Structural Synthesis -- 5.4.5 Disturbance Compensator Parametric Synthesis -- 5.5 Conclusion -- 6: Invariant Relations in the Theory of Optimally Controlled Systems -- 6.1 Introduction -- 6.2 The Problems of Price-Target Invariance in the Theory of Optimal Control -- 6.3 The Problems of Using Singular Controls in Rocket Flight Mechanics -- 6.3.1 Power Consumption in Degeneracy of the Optimal Control of Rocket Thrust in Atmosphere -- 6.3.2 Necessary Conditions for the Optimality of a Singular Control -- 6.3.3 The Problem of Calculating Optimal Trajectories With Singular Arcs -- 6.4 Addition to the Feldbaum Theorem on Number of Switching -- 6.5 Investigation of the Invariance in the Modeling of Functioning in Living Nature -- 6.5.1 Statement of the Anokhin Problem -- 6.5.2 Solution of the Anokhin Problem -- 6.5.3 Features of Expediently Functioning Objects with Redundant Control.

6.5.4 Structure of the Controlling System of an Expediently Functioning Object -- 6.5.5 Hierarchy and Invariance of Expediently Controlled System -- 6.6 Investigation Analysis of Results -- 6.6.1 Mathematical Modeling - A Tool for Research of Complex Systems -- 6.6.2 Optimality and Evolution Selection -- 6.6.3 Hierarchy and Invariance of Expediently Controlled System -- 6.7 Optimal Control Theory as a Tool for Cognition -- 6.8 Is Teleology Theological? -- 6.9 Acknowledgment -- 7: Robust Adaptive Controls for a Class of Nonsquare Memoryless Systems -- 7.1 Introduction -- 7.2 Problem Formulation -- 7.3 Background on Pseudoinverse Model-Based Method -- 7.4 Robust Adaptive Pseudoinverse Model-Based Controllers for SIMO systems -- 7.5 Robust Adaptive Pseudoinverse Model-Based Control of MIMO System -- 7.6 Conclusion -- II: Advances in Control Systems Applications -- 8: Advanced Identification of Impulse Processes in Cognitive Maps -- 8.1 Introduction -- 8.2 Problem Statement -- 8.3 CM Identification Features -- 8.4 Subspace Identification with Regularization -- 8.4.1 Identification for Given Model Dimension -- 8.4.2 Model Dimension Determination -- 8.5 Advanced Subspace Identification -- 8.6 Example -- 8.7 Conclusion -- 9: Strategy for Simulation Complex Hierarchical Systems Based on the Methodologies of Foresight and Cognitive Modeling -- 9.1 Introduction -- 9.2 Theoretical Foundation of Foresight and Cognitive Modeling Methodologies -- 9.2.1 Foresight Methodology of Complex System -- 9.2.2 Methodology of Cognitive Modeling of Complex Systems -- 9.2.3 Relationship of the Education System with the Socio-Economic Environment -- 9.3 Conclusion -- 9.4 Acknowledgment -- 10: Special Cases in Determining the Spacecraft Position and Attitude Using Computer Vision System -- 10.1 Introduction -- 10.2 PnP Problem Statement -- 10.3 PnP Problem Under Uncertainty.

10.4 Rotation Parameterization -- 10.5 Sensitivity of Image -- 10.6 Estimating an Indistinguishable Set -- 10.7 Design of Experiment -- 10.8 Numerical Simulations -- 10.9 Conclusion -- 11: On Determining the Spacecraft Orientation by Information from a System of Stellar Sensors -- 11.1 Introduction -- 11.2 Systems of Coordinates: Formulation of the Problem -- 11.3 Correspondence of Three-Dimensional and Four-Dimensional Parameters of a Group of Three-Dimensional Rotations -- 11.4 Algorithms for Determining the Orientation Parameters of the Spacecraft -- 11.5 Accuracy Analysis of Determining the Parameters of the SC Orientation -- 11.6 Effect of Satellite Initial Orientation Error on the Accuracy of Determining Its Current Orientation -- 11.7 Conclusion -- 12: Control Synthesis of Rotational and Spatial Spacecraft Motion at ApproachingStage of Docking -- 12.1 Introduction -- 12.2 Equation of the Spacecraft Relative Motion in the Docking Stage -- 12.2.1 Equation of the Relative Motion of the Spacecraft Center of Mass -- 12.2.2 Equation of the Spacecraft Relative Angular Motion -- 12.2.3 Control Problem Statement at the Docking Stage -- 12.3 Parameter Estimation of the PSC Rotational Motion -- 12.3.1 Problem Statement of the Angular Motion Parameters Estimation -- 12.3.2 Non-Linear Ellipsoidal Estimation Method -- 12.3.3 Estimation of the Quaternion, Angular Velocity, and Ratios of Inertia Moments -- 12.3.4 Numerical Simulation of the Estimation Algorithm -- 12.4 Synthesis of Spacecraft Motion Control at Docking -- 12.4.1 Synthesis of Motion Control of the Center of Mass of Active Spacecraft -- 12.4.2 Synthesis of Spacecraft Angular Motion Control -- 12.4.3 Computer Simulation of Control Algorithm -- 12.5 Conclusion -- 13: Intelligent Algorithms for the Automation of Complex Biotechnical Objects -- 13.1 Introduction.

13.2 Intelligent Automation Systems for Biotechnical Facilities -- 13.2.1 Traditional Automation Systems for Biotechnical Facilities and their Drawbacks -- 13.2.2 Synthesis of an Intelligent Control System Taking into Account the Forecasting of the Changes in Temperature Images in the Context of a Poultry House -- 13.2.3 Synthesis of the Intelligent Control System Taking into Account the Forecast of the External Natural Disturbances and Radiation in the Context of a Greenhouse -- 13.2.3.1 The Neural Network Forecasting of the External Natural Disturbances -- 13.2.3.2 The Intelligent Solar Radiation Forecasting System -- 13.3 Conclusion -- 14: Automatic Control for theSlow Pyrolysis of Organic Materials with Variable Composition -- 14.1 Introduction -- 14.2 Controlled Pyrolysis Model and Method -- 14.2.1 Problem Definition -- 14.2.2 Purpose and Objectives of the Research -- 14.2.3 Method of Problem Solving -- 14.2.3.1 Facility Scheme Selection -- 14.2.3.2 Control Object Model -- 14.2.3.3 Analysis of the Control Object Model to Solve the Control Task -- 14.2.3.4 Results of Pyrolysis Product Output Modeling -- 14.3 Synthesis of the Plant Control System to Produce Product-Gas -- 14.3.1 The Control Method of Pyrolysis Technology in the Plant -- 14.3.2 A Simulation Model of the Pyrolysis Plant Control System -- 14.3.3 Modeling Results of the Control Process by Pyrolysis Installation -- 14.4 Results and Discussion -- 14.5 Conclusion -- Index -- About the Editors.