Fuzzy Logic Identification And Predictive Control
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Author |
: Jairo Jose Espinosa Oviedo |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 274 |
Release |
: 2007-01-04 |
ISBN-10 |
: 9781846280870 |
ISBN-13 |
: 1846280877 |
Rating |
: 4/5 (70 Downloads) |
Synopsis Fuzzy Logic, Identification and Predictive Control by : Jairo Jose Espinosa Oviedo
Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding "judgement” rather than simple "yes/no”, "on/off” responses: circumstances where a linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose. Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining. This monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
Author |
: Jairo Jose Espinosa Oviedo |
Publisher |
: Springer |
Total Pages |
: 264 |
Release |
: 2009-10-12 |
ISBN-10 |
: 1848007752 |
ISBN-13 |
: 9781848007758 |
Rating |
: 4/5 (52 Downloads) |
Synopsis Fuzzy Logic, Identification and Predictive Control by : Jairo Jose Espinosa Oviedo
Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding "judgement” rather than simple "yes/no”, "on/off” responses: circumstances where a linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose. Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining. This monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
Author |
: Janos Abonyi |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 279 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461200277 |
ISBN-13 |
: 146120027X |
Rating |
: 4/5 (77 Downloads) |
Synopsis Fuzzy Model Identification for Control by : Janos Abonyi
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.
Author |
: Robert Babuška |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 269 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9789401148689 |
ISBN-13 |
: 9401148686 |
Rating |
: 4/5 (89 Downloads) |
Synopsis Fuzzy Modeling for Control by : Robert Babuška
Rule-based fuzzy modeling has been recognised as a powerful technique for the modeling of partly-known nonlinear systems. Fuzzy models can effectively integrate information from different sources, such as physical laws, empirical models, measurements and heuristics. Application areas of fuzzy models include prediction, decision support, system analysis, control design, etc. Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering points of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the design of nonlinear controllers based on fuzzy models. To automatically generate fuzzy models from measurements, a comprehensive methodology is developed which employs fuzzy clustering techniques to partition the available data into subsets characterized by locally linear behaviour. The relationships between the presented identification method and linear regression are exploited, allowing for the combination of fuzzy logic techniques with standard system identification tools. Attention is paid to the trade-off between the accuracy and transparency of the obtained fuzzy models. Control design based on a fuzzy model of a nonlinear dynamic process is addressed, using the concepts of model-based predictive control and internal model control with an inverted fuzzy model. To this end, methods to exactly invert specific types of fuzzy models are presented. In the context of predictive control, branch-and-bound optimization is applied. The main features of the presented techniques are illustrated by means of simple examples. In addition, three real-world applications are described. Finally, software tools for building fuzzy models from measurements are available from the author.
Author |
: Huaguang Zhang |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 423 |
Release |
: 2007-10-17 |
ISBN-10 |
: 9780817645397 |
ISBN-13 |
: 081764539X |
Rating |
: 4/5 (97 Downloads) |
Synopsis Fuzzy Modeling and Fuzzy Control by : Huaguang Zhang
Fuzzy logic methodology has proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology is applicable to many real-world problems, especially in the area of consumer products. This book presents the first comprehensive, unified treatment of fuzzy modeling and fuzzy control, providing tools for the control of complex nonlinear systems. Coverage includes model complexity, model precision, and computing time. This is an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, and also useful for graduate courses in electrical engineering, computer engineering, and computer science.
Author |
: Rómulo Antão |
Publisher |
: Springer |
Total Pages |
: 136 |
Release |
: 2017-07-23 |
ISBN-10 |
: 9789811046339 |
ISBN-13 |
: 9811046336 |
Rating |
: 4/5 (39 Downloads) |
Synopsis Type-2 Fuzzy Logic by : Rómulo Antão
This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets. Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms’ source codes (provided). As such, the book offers avaluable referenceguide for allengineers and researchers in the field ofcomputer science who are interested in intelligent systems, rule-based systems and modeling uncertainty.
Author |
: Mary Margaret Bourke |
Publisher |
: |
Total Pages |
: 724 |
Release |
: 1995 |
ISBN-10 |
: OCLC:70420909 |
ISBN-13 |
: |
Rating |
: 4/5 (09 Downloads) |
Synopsis Self-learning Predictive Control Using Relational-based Fuzzy Logic by : Mary Margaret Bourke
Author |
: John H. Lilly |
Publisher |
: John Wiley & Sons |
Total Pages |
: 199 |
Release |
: 2011-03-10 |
ISBN-10 |
: 9781118097816 |
ISBN-13 |
: 1118097815 |
Rating |
: 4/5 (16 Downloads) |
Synopsis Fuzzy Control and Identification by : John H. Lilly
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 |
: |
Publisher |
: DIANE Publishing |
Total Pages |
: 26 |
Release |
: 2000 |
ISBN-10 |
: 9781428996427 |
ISBN-13 |
: 1428996427 |
Rating |
: 4/5 (27 Downloads) |
Synopsis Self-Tuning of Design Variables for Generalized Predictive Control by :
Author |
: Ruiyun Qi |
Publisher |
: Springer |
Total Pages |
: 293 |
Release |
: 2019-06-11 |
ISBN-10 |
: 9783030198824 |
ISBN-13 |
: 3030198820 |
Rating |
: 4/5 (24 Downloads) |
Synopsis Fuzzy System Identification and Adaptive Control by : Ruiyun Qi
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.