Identification and Adaptive Control for Nonlinear Systems and Applications Books

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Identification and Adaptive Control for Nonlinear Systems and Applications


Identification and Adaptive Control for Nonlinear Systems and Applications
  • Author : Jianhua Zhang
  • Publisher : Academic Press
  • Release : 2022-03-15
  • ISBN : 0128234415
  • Language : En, Es, Fr & De
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Identification and Adaptive Control for Nonlinear Systems and Applications: Applied Mathematics in Control Engineering introduces nonlinear systems concepts, system analysis, system control methods and applications in various fields. The major contribution of the book includes: (1) The basic concepts of nonlinear systems stability analysis and nonlinear systems control method. (2) The stability analysis of complex nonlinear system with adaptive neural networks control. (3) The nonlinear systems adaptive sliding mode controller design of complex nonlinear systems. (4) Some industrial application. The book gives an introduction to basic nonlinear systems architectures for adaptive control methods. Emphasis is placed on the mathematical analysis of these systems, on methods of controlling them for adaptive control and on their application to practical engineering problems in such areas as aircraft path planning. This book enables audience to understand the basic architectures of control science and engineering, and to master classical and advanced design method for nonlinear system. Introduces nonlinear systems concepts, system analysis, system control methods and applications in various fields Presents basic concepts of nonlinear systems stability analysis and nonlinear systems control method Offers practical examples

Nonlinear and Adaptive Control with Applications


Nonlinear and Adaptive Control with Applications
  • Author : Alessandro Astolfi
  • Publisher : Springer Science & Business Media
  • Release : 2007-12-06
  • ISBN : 1848000669
  • Language : En, Es, Fr & De
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The authors here provide a detailed treatment of the design of robust adaptive controllers for nonlinear systems with uncertainties. They employ a new tool based on the ideas of system immersion and manifold invariance. New algorithms are delivered for the construction of robust asymptotically-stabilizing and adaptive control laws for nonlinear systems. The methods proposed lead to modular schemes that are easier to tune than their counterparts obtained from Lyapunov redesign.

System Identification and Adaptive Control


System Identification and Adaptive Control
  • Author : Yiannis Boutalis
  • Publisher : Springer Science & Business
  • Release : 2014-04-23
  • ISBN : 9783319063645
  • Language : En, Es, Fr & De
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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.

Adaptive Sliding Mode Neural Network Control for Nonlinear Systems


Adaptive Sliding Mode Neural Network Control for Nonlinear Systems
  • Author : Yang Li
  • Publisher : Academic Press
  • Release : 2018-11-16
  • ISBN : 9780128154328
  • Language : En, Es, Fr & De
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Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering. Introduces nonlinear systems' basic knowledge, analysis and control methods, along with applications in various fields Offers instructive examples and simulations, including source codes Provides the basic architecture of control science and engineering

Nonlinear Identification and Adaptive Control


Nonlinear Identification and Adaptive Control
  • Author :
  • Publisher :
  • Release : 2004
  • ISBN : OCLC:74262649
  • Language : En, Es, Fr & De
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To address Air Force applications, new methods are developed for system identification (ID) and adaptive control. For linear systems, ID algorithms are developed to obtain consistent parameter estimates, stable models, and optimal inputs. Nonlinear ID methods are developed for block-structured models with measured-input nonlinearities. Subspace ID methods are used to identify linear model components, while optimization methods are used to construct efficient basis functions. Specialized methods are developed to identify nonlinear systems with output nonlinearities, limit cycle dynamics, and hysteresis. Adaptive stabilization algorithms are developed for uncertain linear and nonlinear systems under full-state feedback, as well as linear systems with unknown but bounded relative degree. Extensions to discrete-time systems are addressed. Adaptive command-following algorithms are developed for spacecraft and demonstrated on an experimental testbed. Adaptive disturbance rejection algorithms are developed for tonal and broadband disturbances. Nonlinear control algorithms are developed for shape change actuation for spacecraft. Semistability theory is developed to support research in adaptive control.