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Tuesday, April 21, 2020 | History

1 edition of Nonlinear Identification and Control found in the catalog.

Nonlinear Identification and Control

A Neural Network Approach

by G. P. Liu

  • 40 Want to read
  • 31 Currently reading

Published by Springer London in London .
Written in

    Subjects:
  • Engineering,
  • Physics

  • About the Edition

    The purpose of this monograph is to give the broad aspects of nonlinear identification and control using neural networks. It consists of three parts: - an introduction to the fundamental principles of neural networks; - several methods for nonlinear identification using neural networks; - various techniques for nonlinear control using neural networks. A number of simulated and industrial examples are used throughout the monograph to demonstrate the operation of nonlinear identification and control techniques using neural networks. It should be emphasised that the methods and systems of nonlinear control have not progressed as rapidly as those for linear control. Comparatively speaking, at the present time, they are still in the development stage. We believe that the fundamental theory, various design methods and techniques, and several applications of nonlinear identification and control using neural networks that are presented in this monograph will enable the reader to analyse and synthesise nonlinear control systems quantitatively.

    Edition Notes

    Statementby G. P. Liu
    SeriesAdvances in Industrial Control, Advances in industrial control
    Classifications
    LC ClassificationsTJ212-225
    The Physical Object
    Format[electronic resource] :
    Pagination1 online resource (xx, 210p. 88 illus.)
    Number of Pages210
    ID Numbers
    Open LibraryOL27077588M
    ISBN 101447110765, 1447103459
    ISBN 109781447110767, 9781447103455
    OCLC/WorldCa853271427

    The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W) model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a Cited by: 5.   Nonlinear Dynamical Systems and Control: A Lyapunov-Based Approach - Ebook written by Wassim M. Haddad, VijaySekhar Chellaboina. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Nonlinear Dynamical Systems and Control: A . control strategies that work in “textbook” cases often fail to work in the real world. Two of the factors that often contribute to this difficulty are friction and backlash. These effects are highly nonlinear, difficult to model and analyze even with a „fully nonlinear File Size: KB. In mathematics and science, a nonlinear system is a system in which the change of the output is not proportional to the change of the input. Nonlinear problems are of interest to engineers, biologists, physicists, mathematicians, and many other scientists because most systems are inherently nonlinear in nature. Nonlinear dynamical systems, describing changes in variables .

    For prospective authors interested in publishing in this new series, Emerging Methodologies and Applications in Modelling, please contact Series Editor Quan Zhu or .


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Nonlinear Identification and Control by G. P. Liu Download PDF EPUB FB2

Nonlinear Identification and Control: A Neural Network Approach (Advances in Industrial Control) Hardcover – Octo by G.P. Liu (Author) › Visit Amazon's G.P. Liu Page. Find all the books, read about the author, and more.

See search results for this Cited by: Nonlinear Identification and Control A Neural Network Approach. Authors: Liu, G.P. Free Preview. Buy this book eB89 € price for Spain (gross) Buy eBook ISBN ; Digitally watermarked, DRM-free; Included format: PDF; ebooks can be.

Nonlinear control has had a long gestation period but much ofthe past has been concerned with methods that involve formal nonlinear functional model representations. It seems more likely that the breakthough will come through the use of other more flexible and amenable nonlinear system modelling tools.

field of nonlinear identification and control. Methods are illustrated with relevant simulation examples, and the final chapter presents a simulated and experimental case Nonlinear Identification and Control book based on a combustion process wherein a net-work with sinusoidal basis functions is used as an output predictor.

Unfortu-nately,theexperimentalcasestudydid. Part of the Stochastic Modelling and Applied Probability book series (SMAP, volume 34) Abstract The identification of a functional model involves estimating functions which are densities of a stationary distribution or regression functions; criteria for uniform consistency are sometimes useful here since, in practice, calculations are performed Author: Marie Duflo.

This chapter has introduced the basic concepts related with the use of neural net works for nonlinear systems identification, and has briefly reviewed neuro-control approaches. Several important issues for the design of data-driven models, as neural networks are, such as data acquisition and the design of excitation signals could not be covered here, but the will be Cited by: This is simply the best book written on nonlinear control theory.

The contents form the basis for feedback linearization techniques, nonlinear observers, sliding mode control, understanding relative degree, nonminimum phase systems, exact linearization, and a host of other topics. A careful reading of this book will provide vast rewards.

Identification and Control of Nonlinear Systems Using Neural Networks: A Singularity-Free Approach Abstract: In this paper, identification and control for a class of nonlinear systems with unknown constant or variable control gains are investigated. By reformulating the original system dynamic equation into a new form with a unit control gain Cited by: 4.

Home Browse by Title Books Nonlinear process control. Nonlinear process control January Artificial neural network for nonlinear process identification and control. Ted Su, Thomas J. McAvoy; Januarypp Nonlinear MIMO control of a continuous cooling crystallizer, Modelling and Simulation in Engineering,( simple, reasonably general, nonlinear system theory could be developed.

Hand in hand with this viewpoint was the feeling that many of the approaches useful for linear systems ought to be extensible to the nonlinear theory. This is a key point if the theory is to be used by practitioners as well as by researchers. The book will be of major interest to all those who are concerned with nonlinear system identification whatever their activity areas.

This is particularly the case for educators in electrical, mechanical, chemical Nonlinear Identification and Control book biomedical engineering and for practising engineers in process, aeronautic, aerospace, robotics and vehicles control. Nonlinear Identification and Control: A Neural Network Approach [Book Review] Article in IEEE Control Systems Magazine 22(5) November with 20.

e-books in Control Systems category. An introduction to the field of intelligent control with a broad treatment of topics by several authors (including hierarchical / distributed intelligent control, fuzzy control, expert control, neural networks, planning systems, and applications).

Download Citation | Book review: Nonlinear identification and control-a neural network approach | This paper derives optimal controls for the thrusted skate between any two points in. Nonlinear and Optimal Control Systems offers a self-contained introduction to analysis techniques used in the design of nonlinear and optimal feedback control systems, with a solid emphasis on the fundamental topics of stability, controllability, optimality.

Probably the best book to start with nonlinear control Nonlinear systems S. Sastry - Springer Verlag, Good general book, a bit harder than Khalil’s Mathematical Control Theory - E.D. Sontag - Springer, Mathematically oriented, Can be downloaded at. Nonlinear Dynamical Control Systems.

This textbook on the differential geometric approach to nonlinear control grew out of a set of lecture notes, which were prepared for a course on nonlinear system theory, given by us for the first time during the fall semester of INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING Int.

Adapt. Control Signal Process. ; – Published online in Wiley InterScience () BOOK REVIEW NONLINEAR IDENTIFICATION AND CONTROL: A NEURAL NETWORK APPROACH, G.P.

Liu, ADVANCES IN INDUSTRIAL CONTROL Cited by: 1. The purpose of this book is to present a self-contained description of the fun damentals of the theory of nonlinear control systems, with special emphasis on the differential geometric approach.

The book is intended as a graduate text as weil as a reference to scientists and engineers involved in the analysis and design of feedback systems.

The first version of this book /5(6). Nonlinear grey-box models — Represent your nonlinear system using ordinary differential or difference equations (ODEs) with unknown parameters.

Nonlinear model identification requires uniformly sampled time-domain data. Your data can have one or more input and output channels. Comprised of 14 chapters, this book begins by describing the application of nonlinear programming to an optimum design problem coming from mechanical engineering.

The reader is then introduced to a nonlinear regulator design for magnetic suspension; optimal control solution of the automotive emission-constrained minimum fuel problem; and Book Edition: 1. Books shelved as non-linear: An Ishmael of Syria by Asaad Almohammad, Slaughterhouse-Five by Kurt Vonnegut Jr., A Visit from the Goon Squad by Jennifer E.

Nonlinear and Optimal Control Theory: Lectures Given at the C.I.M.E. Summer School Held in Cetraro, Italy, June, Issue C.I.M.E. Foundation Subseries Lecture Notes in Mathematics, ISSN Nonlinear Dynamical Systems and Control presents and develops an extensive treatment of stability analysis and control design of nonlinear dynamical systems, with an emphasis on Lyapunov-based methods.

Dynamical system theory lies at the heart of mathematical sciences and engineering. Summary: The purpose of this monograph is to give the broad aspects of nonlinear identification and control using neural networks. It uses a number of simulated and industrial examples throughout, to demonstrate the operation of nonlinear identification and control techniques using neural networks.

UNESCO – EOLSS SAMPLE CHAPTERS CONTROL SYSTEMS, ROBOTICS, AND AUTOMATION - Vol. VI - Identification of Nonlinear Systems - H. Unbehauen ©Encyclopedia of Life Support Systems (EOLSS) Parameter Estimation for Non-LIP-Type Models Differential Neural Networks for Robust Nonlinear Control: Identification, State Estimation and Trajectory Tracking Alexander S.

Poznyak, Edgar N. Sanchez, Wen Yu This volume deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space.

NONLINEAR PROGRAMMING FOR SYSTEM IDENTIFICATION. Gupta Systems Control, Inc. (Vt), Page Mill Palo Alto, CAUSA Road, Abstract. Numerical procedures for dynamic system identification are discussed.

Efficient algorithms for static least-squares problems provide a starting point for dynamic systems nonlinear programming by: 1. Nonlinear Systems: Analysis, Stability, and Control - Ebook written by Shankar Sastry. Read this book using Google Play Books app on your PC, android, iOS devices.

Download for offline reading, highlight, bookmark or take notes while you read Nonlinear Systems: Analysis, Stability, and : Shankar Sastry. Industrial Use of System ID • Process control - most developed ID approaches – all plants and processes are different – need to do identification, cannot spend too much time on each – industrial identification tools • Aerospace – white-box identification, specially designed programs of tests • AutomotiveFile Size: KB.

Nonlinear Identification and Control: A Neural Network Approach The purpose of this monograph is to give the broad aspects of Nonlinear identification and control using neural networks. It uses a number of simulated and industrial examples throughout, to demonstrate the operation of Nonlinear identification and control techniques using neural.

Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains.

This book is written with an emphasis on making the algorithms accessible so that they can be applied and. • Adaptive control of nonlinear plants: Krstic, Kanellakopoulos and Kokotovi´c, Nonlinear and Adaptive Control Design, Wiley, (Referred to as “KKK book” below.) Contains some more advanced adaptive control mate-rial, and covers some nonlinear systems and control theory concepts as well.

Most system identification algorithms are of this type. In the context of nonlinear system identification Jin et al. describe greybox modeling by assuming a model structure a priori and then estimating the model parameters.

Parameter estimation is relatively easy if the model form is known but this is rarely the case. System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs.

The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine.

I used a portfolio of books, since my classes in control engineering covered a variety of topics. The three main books that I used were: [1] R. Dorf and R. Bishop. Modern Control Systems. Pearson Education, Upper Saddle River, NJ, elev.

It is clear that this new controller is very useful for identification and control of systems with unknown and highly nonlinear dynamics [10].

Sedighizadeh et al [] used idea of Self-tuning control of nonlinear systems using neural network adaptive frame wavelets for identification and control of WECS.

This book serves as a most promising source that combines process sys-tems engineering with nonlinear systems and control theory. This combina-tion is carried through in the book by providing the reader with references to linear time invariant control theory.

The nonlinear passivity theory con. These two identification parts together with the optimization part enable the algorithm to perform -after an initial phase where the configuration of the system and of the rooms are stored - an energy-optimal, dynamic heating of buildings in order to control the temperatures of each room separately w.r.t.

temporally varying reference inputs. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence.

The critical book on the matter is Lennart Ljung, "System Identification: Theory for the User". In reality this is an active area of research and you may be better off looking at journals, but it depends on what you want from your models.People The Nonlinear Systems Laboratory is headed by Professor Jean-Jacques s and affiliates are Gabriel Bousquet ([email protected])Soon-Jo Chung ([email protected])Joanna Cohen ([email protected])Khalid El-Rifai ([email protected])Winfried Lohmiller ([email protected])Quang-Cuong Pham ([email protected])Jonathan Soto ([email protected])Nicolas Tabareau ([email protected]).available for the statistical analysis of static and dynamic nonlinear systems including linearisation methods, system identification algorithms, and stochastic control.

Static Nonlinear Systems Consider the system illustrated in Fig. 1 where u (t) is applied as an input to a single—valued instantaneous nonlinear element NC) to produce.