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Author | : Ruiyun Qi |
Publisher | : |
Total Pages | : 282 |
Release | : 2019 |
Genre | : Artificial intelligence |
ISBN | : 9783030198831 |
Download Fuzzy System Identification and Adaptive Control Book in PDF, ePub and Kindle
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. 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.
Author | : Yiannis Boutalis |
Publisher | : Springer Science & Business |
Total Pages | : 316 |
Release | : 2014-04-23 |
Genre | : Technology & Engineering |
ISBN | : 3319063642 |
Download System Identification and Adaptive Control Book in PDF, ePub and Kindle
Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in: • contemporary power generation; • process control and • conventional benchmarking problems. Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results.
Author | : |
Publisher | : |
Total Pages | : |
Release | : 1998 |
Genre | : |
ISBN | : |
Download On Fuzzy Logic Systems, Nonlinear System Identification, and Adaptive Control Book in PDF, ePub and Kindle
Author | : Ruiyun Qi |
Publisher | : Springer |
Total Pages | : 282 |
Release | : 2019-06-11 |
Genre | : Technology & Engineering |
ISBN | : 3030198820 |
Download Fuzzy System Identification and Adaptive Control Book in PDF, ePub and Kindle
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. 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.
Author | : John H. Lilly |
Publisher | : John Wiley & Sons |
Total Pages | : 199 |
Release | : 2011-03-10 |
Genre | : Technology & Engineering |
ISBN | : 1118097815 |
Download Fuzzy Control and Identification Book in PDF, ePub and Kindle
This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models. Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic.
Author | : Janos Abonyi |
Publisher | : Springer Science & Business Media |
Total Pages | : 279 |
Release | : 2012-12-06 |
Genre | : Technology & Engineering |
ISBN | : 146120027X |
Download Fuzzy Model Identification for Control Book in PDF, ePub and Kindle
This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files create a computational platform for exploration of the concepts and algorithms.
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Publisher | : |
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Download Identification and Adaptive Control of a Class of Nonlinear Systems with Fuzzy System Models Book in PDF, ePub and Kindle
HKUST Call Number: Thesis ELEC 2001 Wan.
Author | : |
Publisher | : |
Total Pages | : |
Release | : 1998 |
Genre | : |
ISBN | : |
Download On Fuzzy Logic Systems, Nonlinear System Identification, and Adaptive Control Book in PDF, ePub and Kindle
Author | : Ruiyun Qi |
Publisher | : Springer |
Total Pages | : 0 |
Release | : 2020-06-10 |
Genre | : Technology & Engineering |
ISBN | : 9783030198848 |
Download Fuzzy System Identification and Adaptive Control Book in PDF, ePub and Kindle
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. 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.
Author | : Dimiter Driankov |
Publisher | : Physica |
Total Pages | : 421 |
Release | : 2013-04-17 |
Genre | : Computers |
ISBN | : 3790818860 |
Download Advances in Fuzzy Control Book in PDF, ePub and Kindle
Model-based fuzzy control uses a given conventional or a fuzzy open loop of the plant under control in order to derive the set of fuzzy if-then rules constituting the corresponding fuzzy controller. Furthermore, of central interest are the consequent stability, performance, and robustness analysis of the resulting closed loop system involving a conventional model and a fuzzy controller, or a fuzzy model and a fuzzy controller. The major objective of the model-based fuzzy control is to use the full available range of existing linear and nonlinear design of such fuzzy controllers which have better stability, performance, and robustness properties than the corresponding non-fuzzy controllers designed by the use of these same techniques.