Название: Fuzzy System Identification and Adaptive Control Автор: Ruiyun Qi, Gang Tao Издательство: Springer Год: 2019 Страниц: 293 Язык: английский Формат: pdf (true), epub Размер: 26.4 MB
This book provides readers with a systematic and unified framework for identification and adaptive control of Takagi–Sugeno (T–S) fuzzy systems. Its design techniques help readers applying these powerful tools to solve challenging nonlinear control problems. The book embodies a systematic study of fuzzy system identification and control problems, using T–S fuzzy system tools for both function approximation and feedback control of nonlinear systems. Alongside this framework, the book also:
introduces basic concepts of fuzzy sets, logic and inference system; discusses important properties of T–S fuzzy systems; develops offline and online identification algorithms for T–S fuzzy systems; investigates the various controller structures and corresponding design conditions for adaptive control of continuous-time T–S fuzzy systems; develops adaptive control algorithms for discrete-time input–output form T–S fuzzy systems with much relaxed design conditions, and discrete-time state-space T–S fuzzy systems; and designs stable parameter-adaptation algorithms for both linearly and nonlinearly parameterized T–S fuzzy systems.
In adaptive approximation-based control designs, neural networks, fuzzy systems, or other traditional function approximators such as polynomials, splines, and wavelets are employed to approximate unknown nonlinear functions. From a mathematical perspective, those function approximators can be used in a similar way when their role is to approximate some static nonlinear functions in dynamic nonlinear systems. Actually, most adaptive approximation-based control designs have used neural networks or fuzzy systems in this way: for some classes of nonlinear systems (strict-feedback, pure feedback, feedback linearizable, etc.), suitable nonlinear control techniques, such as backstepping and feedback linearization, are applied to develop nonlinear controllers where the unknown nonlinear functions are approximated by neural networks or fuzzy systems. The parameters or weights of neural networks/fuzzy systems are adaptively adjusted to compensate the nonlinear effects to achieve desirable control performance.
The authors address adaptive fault compensation problems for T–S fuzzy systems subject to actuator faults. They cover a broad spectrum of related technical topics and to develop a substantial set of adaptive nonlinear system control tools.
Fuzzy System Identification and Adaptive Control helps engineers in the mechanical, electrical and aerospace fields, to solve complex control design problems. The book can be used as a reference for researchers and academics in nonlinear, intelligent, adaptive and fault-tolerant control.
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