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Differential Neural Networks for Robust Nonlinear Control

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Release : 2001
Genre : Science
Kind : eBook
Book Rating : 295/5 ( reviews)

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Book Synopsis Differential Neural Networks for Robust Nonlinear Control by : Alexander S. Poznyak

Download or read book Differential Neural Networks for Robust Nonlinear Control written by Alexander S. Poznyak. This book was released on 2001. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents: Theoretical Study: Neural Networks Structures; Nonlinear System Identification: Differential Learning; Sliding Mode Identification: Algebraic Learning; Neural State Estimation; Passivation via Neuro Control; Neuro Trajectory Tracking; Neurocontrol Applications: Neural Control for Chaos; Neuro Control for Robot Manipulators; Identification of Chemical Processes; Neuro Control for Distillation Column; General Conclusions and Future Work; Appendices: Some Useful Mathematical Facts; Elements of Qualitative Theory of ODE; Locally Optimal Control and Optimization. Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks.

Neural Network Based Robust Nonlinear Control

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Author :
Release : 2006
Genre : Adaptive control systems
Kind : eBook
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Book Synopsis Neural Network Based Robust Nonlinear Control by : Nishant Unnikrishnan

Download or read book Neural Network Based Robust Nonlinear Control written by Nishant Unnikrishnan. This book was released on 2006. Available in PDF, EPUB and Kindle. Book excerpt: "Online trained neural networks have become popular in recent years in the design of robust and adaptive controllers for dynamic systems with uncertainties due to their universal function approximation capabilities. This research explores the application of online neural networks for the design of model following controllers and for dynamic reoptimization of a Single Network Adaptive Critic (SNAC) optimal controller. Model following controllers for a general class of nonlinear systems with unknown uncertainties in their modeling equations have been developed in this research. A desirable characteristic of the model following controller scheme elaborated in this work is that it can be used in conjunction with any known control design technique. This research also discusses a technique that dynamically re-optimizes a Single Network Adaptive Critic controller. The SNAC based optimal controller designed for the nominal plant model no more retains optimality in the presence of uncertainties/unmodeled dynamics that may creep up in the system equations during operation. This necessitates the application of online function approximating neural networks that can help in SNAC reoptimization. Neural network weight update rules for continuous and discrete time systems have been derived using Lyapunov theory that guarantees both the stability of error dynamics and boundedness of the neural network weights. Detailed proofs and numerical simulations of the online weight update rules on various engineering problems have been provided in this document"--Abstract, leaf iii.

Robust and Fault-Tolerant Control

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Release : 2019-03-16
Genre : Technology & Engineering
Kind : eBook
Book Rating : 69X/5 ( reviews)

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Book Synopsis Robust and Fault-Tolerant Control by : Krzysztof Patan

Download or read book Robust and Fault-Tolerant Control written by Krzysztof Patan. This book was released on 2019-03-16. Available in PDF, EPUB and Kindle. Book excerpt: Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include: a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.

Dynamic Neural Network-based Robust Control Methods for Uncertain Nonlinear Systems

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Release : 2012
Genre :
Kind : eBook
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Book Synopsis Dynamic Neural Network-based Robust Control Methods for Uncertain Nonlinear Systems by : Huyen T. Dinh

Download or read book Dynamic Neural Network-based Robust Control Methods for Uncertain Nonlinear Systems written by Huyen T. Dinh. This book was released on 2012. Available in PDF, EPUB and Kindle. Book excerpt: This result is achieved by combining the DNN-identification strategy with a RISE (Robust Integral of the Sign of the Error) controller. In Chapters 4 and 5, a class of second-order uncertain nonlinear systems with partially unmeasurable states is considered. A DNN-based observer is developed to estimate the missing states in Chapter 4, and the DNN-based observer is developed for an output feedback (OFB) tracking control method in Chapter 5. In Chapter 6, an OFB control method is developed for uncertain nonlinear systems with time-varying input delays. In all developed approaches, weights of the DNN can be adjusted on-line: no off-line weight update phase is required. Chapter 7 concludes the proposal by summarizing the work and discussing some future problems that could be further investigated.

Neural Network-Based State Estimation of Nonlinear Systems

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Release : 2009-12-04
Genre : Technology & Engineering
Kind : eBook
Book Rating : 382/5 ( reviews)

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Book Synopsis Neural Network-Based State Estimation of Nonlinear Systems by : Heidar A. Talebi

Download or read book Neural Network-Based State Estimation of Nonlinear Systems written by Heidar A. Talebi. This book was released on 2009-12-04. Available in PDF, EPUB and Kindle. Book excerpt: "Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

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