Application-oriented monograph focussing on a novel and complex type of control systems
Written on an engineering level, including fundamentals, advanced methods and applications
The applied techniques originate from new methods like artificial intelligence, fuzzy logic, neural networks etc.
This application-oriented monograph focuses on a novel and complex type of control systems. Written on an engineering level, including fundamentals, advanced methods and applications, the book applies techniques originating from new methods such as artificial intelligence, fuzzy logic, neural networks etc.
|Control theory of nonlinear systems, in which either the linear part is known but the relevant nonlinearities in place, kind or parameters are unknown, or both the linear and the nonlinear parts are partially or even most unknown, is a new, demanding and highly interesting field. This book treats the problem by focussing on the role of learning. Intelligent learning techniques are able to determine the unknown components of nonlinear systems. These processes are always stable and convergent. The methods presented can be used both on-line and off-line. They have applications in mechatronics, hydraulics and combustion engines.
1 Introduction — Control Aspects.- 2 Motion Control.- 3 Learning in Control Engineering.- 4 Nonlinear Function Approximators.- 5 Systematic Intelligent Observer Design.- 6 Identification of Separable Nonlinearities.- 7 Identification and Compensation of Friction.- 8 Detection and Identification of Backlash.- 9 Identification of Isolated Nonlinearities in Rolling Mills.- 10 Input-Output Linearization: an Introduction.- 11 Stable Model Reference Neurocontrol.- 12 Dynamic Neural Network Compositions.- 13 Further Strategies for Nonlinear Control with Neural Networks.- List of Figures.
1 Introduction - Control Aspects.- 2 Motion Control.- 3 Learning in Control Engineering.- 4 Nonlinear Function Approximators.- 5 Systematic Intelligent Observer Design.- 6 Identification of Separable Nonlinearities.- 7 Identification and Compensation of Friction.- 8 Detection and Identification of Backlash.- 9 Identification of Isolated Nonlinearities in Rolling Mills.- 10 Input-Output Linearization: an Introduction.- 11 Stable Model Reference Neurocontrol.- 12 Dynamic Neural Network Compositions.- 13 Further Strategies for Nonlinear Control with Neural Networks.- List of Figures.
Inhaltsverzeichnis
1 Introduction ¿ Control Aspects.- 2 Motion Control.- 3 Learning in Control Engineering.- 4 Nonlinear Function Approximators.- 5 Systematic Intelligent Observer Design.- 6 Identification of Separable Nonlinearities.- 7 Identification and Compensation of Friction.- 8 Detection and Identification of Backlash.- 9 Identification of Isolated Nonlinearities in Rolling Mills.- 10 Input-Output Linearization: an Introduction.- 11 Stable Model Reference Neurocontrol.- 12 Dynamic Neural Network Compositions.- 13 Further Strategies for Nonlinear Control with Neural Networks.- List of Figures.
Klappentext
Control theory of nonlinear systems, in which either the linear part is known but the relevant nonlinearities in place, kind or parameters are unknown, or both the linear and the nonlinear parts are partially or even most unknown, is a new, demanding and highly interesting field. This book treats the problem by focussing on the role of learning. Intelligent learning techniques are able to determine the unknown components of nonlinear systems. These processes are always stable and convergent. The methods presented can be used both on-line and off-line. They have applications in mechatronics, hydraulics and combustion engines.
Application-oriented monograph focussing on a novel and complex type of control systemsWritten on an engineering level, including fundamentals, advanced methods and applicationsThe applied techniques originate from new methods like artificial intelligence, fuzzy logic, neural networks etc.
