Advanced Control Systems : theory and applications

Front Cover
Advanced Control Systems: Theory and Applications
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 thePresence of Pulse Perturbations
		1.5 Conclusion
		References
	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
		References
	3 Principle of Time Stretching for Motion Control in Condition of Conflict
		3.1 Introduction
		3.2 Equivalence of the Pursuit Game with Delay ofInformation 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
		References
	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
		References
	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
		References
	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
		References
	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
		References
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
		References
	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
		References
	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
		References
	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
		References
	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
		References
	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
		References
	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
		References
	Index
	About the Editors
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