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Modern Control Theory

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ISBN-10: 3540239510

ISBN-13: 9783540239512

Edition: 2005

Authors: Zdzislaw Bubnicki

List price: $54.99
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This compact and uniform textbook presents the contemporary state of the art of control theory and its applications. It introduces traditional problems useful in the automatic control of technical processes, as well as current issues, such as decision taking in conditions of uncertainty, use of artificial intelligence methods, or control of complex operations. The methods covered are introduced in a practice-oriented way that allows the reader to easily apply them for the determination of decision algorithms in computer control and management systems. This concise textbook is aimed at students of automatics, robotics, control engineering, and computer sciences.
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Book details

List price: $54.99
Copyright year: 2005
Publisher: Springer Berlin / Heidelberg
Publication date: 6/23/2005
Binding: Hardcover
Pages: 424
Size: 6.10" wide x 9.25" long x 0.46" tall
Weight: 3.806

Professor Zdzislaw Bubnicki died on 12th March 2006.

General Characteristic of Control Systems
Subject and Scope of Control Theory
Basic Terms
Control Plant
Classification of Control Systems
Classification with Respect to Connection Between Plant and Controller
Classification with Respect to Control Goal
Other Cases
Stages of Control System Design
Relations Between Control Science and Related Areas in Science and Technology
Character, Scope and Composition of the Book
Formal Models of Control Systems
Description of a Signal
Static Plant
Continuous Dynamical Plant
State Vector Description
"Input-output" Description by Means of Differential Equation
Operational Form of "Input-output" Description
Discrete Dynamical Plant
Control Algorithm
Introduction to Control System Analysis
Continuous System
Discrete System
Control for the Given State (the Given Output)
Control of a Static Plant
Control of a Dynamical Plant. Controllability
Control of a Measurable Plant in the Closed-loop System
Control with an Observer in the Closed-loop System
Structural Approach
Additional Remarks
Optimal Control with Complete Information on the Plant
Control of a Static Plant
Problems of Optimal Control for Dynamical Plants
Discrete Plant
Continuous Plant
Principle of Optimality and Dynamic Programming
Bellman Equation
Maximum Principle
Linear-quadratic Problem
Parametric Optimization
General Idea of Parametric Optimization
Continuous Linear Control System
Discrete Linear Control System
System with the Measurement of Disturbances
Typical Forms of Control Algorithms in Closed-loop Systems
Linear Controller
Two-position Controller
Neuron-like Controller
Fuzzy Controller
Application of Relational Description of Uncertainty
Uncertainty and Relational Knowledge Representation
Analysis Problem
Decision Making Problem
Dynamical Relational Plant
Application of Probabilistic Descriptions of Uncertainty
Basic Problems for Static Plant and Parametric Uncertainty
Basic Problems for Static Plant and Non-parametric Uncertainty
Control of Static Plant Using Results of Observations
Indirect Approach
Direct Approach
Application of Games Theory
Basic Problem for Dynamical Plant
Stationary Stochastic Process
Analysis and Parametric Optimization of Linear Closed-loop Control System with Stationary Stochastic Disturbances
Non-parametric Optimization of Linear Closed-loop Control System with Stationary Stochastic Disturbances
Relational Plant with Random Parameter
Uncertain Variables and Their Applications
Uncertain Variables
Application of Uncertain Variables to Analysis and Decision Making (Control) for Static Plant
Parametric Uncertainty
Non-parametric Uncertainty
Relational Plant with Uncertain Parameter
Control for Dynamical Plants. Uncertain Controller
Fuzzy Variables, Analogies and Soft Variables
Fuzzy Sets and Fuzzy Numbers
Application of Fuzzy Description to Decision Making (Control) for Static Plant
Plant without Disturbances
Plant with External Disturbances
Comparison of Uncertain Variables with Random and Fuzzy Variables
Comparisons and Analogies for Non-parametric Problems
Introduction to Soft Variables
Descriptive and Prescriptive Approaches. Quality of Decisions
Control for Dynamical Plants. Fuzzy Controller
Control in Closed-loop System. Stability
General Problem Description
Stability Conditions for Linear Stationary System
Continuous System
Discrete System
Stability of Non-linear and Non-stationary Discrete Systems
Stability of Non-linear and Non-stationary Continuous Systems
Special Case. Describing Function Method
Stability of Uncertain Systems. Robustness
An Approach Based on Random and Uncertain Variables
Convergence of Static Optimization Process
Adaptive and Learning Control Systems
General Concepts of Adaptation
Adaptation via Identification for Static Plant
Adaptation via Identification for Dynamical Plant
Adaptation via Adjustment of Controller Parameters
Learning Control System Based on Knowledge of the Plant
Knowledge Validation and Updating
Learning Algorithm for Decision Making in Closed-loop System
Learning Control System Based on Knowledge of Decisions
Knowledge Validation and Updating
Learning Algorithm for Control in Closed-loop System
Intelligent and Complex Control Systems
Introduction to Artificial Intelligence
Logical Knowledge Representation
Analysis and Decision Making Problems
Logic-algebraic Method
Neural Networks
Applications of Neural Networks in Control Systems
Neural Network as a Controller
Neural Network in Adaptive System
Decomposition and Two-level Control
Control of Complex Plant with Cascade Structure
Control of Plant with Two-level Knowledge Representation
Control of Operation Systems
General Characteristic
Control of Task Distribution
Control of Resource Distribution
Control of Assignment and Scheduling
Control of Allocation in Systems with Transport
Control of an Assembly Process
Application of Relational Description and Uncertain Variables
Application of Neural Network