1 edition of Towards stable adaptive control for nonlinear systems found in the catalog.
Written in English
|Statement||by Apostolos V. Papadoulis|
|The Physical Object|
|Pagination||vi, 200 leaves :|
|Number of Pages||200|
An adaptive system for linear systems with unknown parameters is a nonlinear system. The analysis of such adaptive systems requires similar techniques to analyse nonlinear systems. Therefore it is natural to treat adaptive control as a part of nonlinear control systems. Nonlinear and Adaptive Control Systems treats nonlinear control and adaptive control in a unified framework, presenting Cited by: Nonlinear and Adaptive Control Design is an absolute must for researchers and graduate students with an interest in nonlinear systems, adaptive control, stability and differential equations and for anyone who would like to find out about the new and exciting advances in these areas/5(2).
STABLE ADAPTIVE CONTROL AND ESTIMATION FOR NONLINEAR SYSTEMS: NEURAL AND FUZZY APPROXIMATOR TECHNIQUES and some C code)* to generate the plots for the examples and simulations in the book. The files are organized by chapter, where each file can be retrieved by clicking on its name. Decentralized Systems * For MATLAB product information. Adaptive control of feedback linearizable nonlinear systems with application to flight control. Towards applied nonlinear adaptive control. Annual Reviews in Control, Vol. 32, No. 2. Adaptive Control for Systems with Slow Reference by:
Description: Adaptive internal model control; An algorithm for robust adaptive control with less prior knowledge; Adaptive variable structure control; Adaptive stabilization of uncertain discrete time systems via switching control: the method of localization; Adaptive nonlinear control: passivation and small gain techniques; Active identification for control of discrete-time uncertain nonlinear systems; Optimal adaptive traking for nonlinear systems; Stable adaptive systems . ♥ Book Title: Stable Adaptive Neural Network Control ♣ Name Author: S.S. Ge ∞ Launching: Info ISBN Link: ⊗ Detail ISBN code: ⊕ Number Pages: Total sheet ♮ News id: k2j1BwAAQBAJ Download File Start Reading ☯ Full Synopsis: "Recent years have seen a rapid development of neural network control tech niques and their successful applications.
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Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques brings together these two different but equally useful approaches to the control of nonlinear systems in order to provide students and practitioners with the background necessary to understand and contribute to this emerging by: Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques.
Stable Adaptive Control and Estimation for Nonlinear Systems.: Neural and Fuzzy Approximator Techniques. Author (s): Jeffrey T. Spooner. Author(s): Jeffrey T. Spooner, Manfredi Maggiore, Raúl Ordóñez, Kevin M.
Passino. Published Online: 28 MAY Print ISBN: Online ISBN: DOI: / Series Editor(s): Simon Haykin. STABLE ADAPTIVE CONTROL AND ESTIMATION FOR NONLINEAR SYSTEMS Neural and Fuzzy Approximator Techniques Jeffrey T. Spooner Sandia National Laboratories Manfredi Maggiore University of Toronto Raul Ordonez University of Dayton Kevin M.
Passino The Ohio State University A JOHN WILEY & SONS, INC., PUBLICATION WILEY-INTERSCIENCE. Stable adaptive control for nonlinear multivariable systems with a triangular control structure. Abstract: A stable adaptive controller is developed for a class of nonlinear multivariable systems using nonlinearly parametrized function approximators.
By utilizing the system triangular property, integral-type Lyapunov functions are introduced for deriving the control structure and adaptive laws without the need of estimating the "decoupling matrix" of the multivariable nonlinear by: In , the fully distributed adaptive consensus control of a class of high-order nonlinear systems with a directed topology and unknown control directions is Author: Zhengtao Ding.
Spooner JT, Maggiore M, Ordonez R, Passino KM () Stable adaptive control and estimation for nonlinear systems. Wiley, New York Google Scholar Townley S () An example of a globally stabilizing adaptive controller with a generically destabilizing parameter estimate.
Spooner JT, Maggiore M, Ordonez R, Passino KM () Stable adaptive control and estimation for nonlinear systems. Wiley, New York CrossRef Google Scholar Townley S () An example of a globally stabilizing adaptive controller with a generically destabilizing parameter estimate.
Purchase Adaptive Control Systems - 1st Edition. Print Book & E-Book. ISBNNonlinear controllaws have been implemented for sophisticated flight control systems on board helicopters, and vertical take offand landing aircraft; adaptive, nonlinearcontrollaws havebeen implementedfor robot manipulators operating either singly, or in cooperation on a multi-fingered robot hand; adaptive control laws have been implemented Brand: Springer-Verlag New York.
The area of adaptive nonlinear control has moved on quickly since the early s. The development of most adaptive nonlinear controller designs was based on Lyapunov methods. This chapter proposes methods to systematically design stabilizing adaptive controllers for new classes of nonlinear systems using passivation and small gain techniques.
Nonlinear and Adaptive Control with Applications provides a detailed treatment of the design of robust adaptive controllers for nonlinear systems with uncertainties. The authors employ a new tool based on the ideas of system immersion and manifold invariance.
Departing, in part, from the Lyapunov-function approach of classical control, new algorithms are delivered for the construction of.
Nonlinear adaptive control, adaptive observers, uncertain nonlinear systems. INTRODUCTION At the beginning of the eigthies two important classes of nonlinear systems were defined for which constructive procedures to design nonlinear state feedback control were determined: feedback linearizable systems and input-output feedback linearizable by: Zheng Y, Liu Y, Tong S and Li T Combined adaptive fuzzy control for uncertain MIMO nonlinear systems Proceedings of the conference on American Control Conference, () Santillo M, D'Amato A and Bernstein D System identification using a retrospective correction filter for adaptive feedback model updating Proceedings of the E.
Lavretsky 2 Robust and Adaptive Control Workshop Adaptive Control: Introduction, Overview, and Applications Course Overview • Motivating Example • Review of Lyapunov Stability Theory – Nonlinear systems and equilibrium points –Linearization – Lyapunov’s direct methodFile Size: 2MB.
A very good book for neophytes in adaptive control as well as those who want to refresh concepts learned ages ago. The book is self-contained insofar as the history of adaptive control and the preliminaries are concerned.
A wide class of systems has been analysed. The concepts are initially illustrated for simple systems, and the restrictions imposed on the class of systems is gradually Cited by: Adaptive control can be used in the case of complete unknown a.
u = −cx−axˆ (13) aˆ˙ = x2 (14) If we let a˜ = a−aˆ, the closed-loop system is described by x˙ = −cx+ ˜ax (15) ˜a˙ = −x2 (16) This adaptive system is nonlinear, even though the original uncertain system is linear.
This adaptive system is stable, but how to show it. 7File Size: KB. applicable to most time-invariant linear models, ‘adaptive nonlinear control’ is more recent and is restricted to special classes of nonlinear models.
In both linear and nonlinear models the unknown parameters are assumed to appear linearly. The adaptive control problem for nonlinear models is much more difficult than for linear Size: KB.
Nonlinear systems Khalil - Prentice-Hall, 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. Stable adaptive neural control scheme for nonlinear systems Abstract: Based on the Lyapunov synthesis approach, several adaptive neural control schemes have been developed during the last few years.
So far, these schemes have been applied only to simple classes of nonlinear by:. MFAC is a novel adaptive control method for the unknown discrete-time nonlinear systems with time-varying parameters and time-varying structure, and the design and analysis of MFAC merely depend on the measured input and output data of the controlled plant, which makes it .Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques.
Written for practicing engineers and graduate students, this text brings together adaptive control with neural networks and fuzzy systems for the control of nonlinear systems. The authors present a control methodology that may be verified.In this paper, we solve the H∞ robust optimal control problem for discrete-time nonlinear systems with control saturation constraints using the iterative adaptive dynamic programming algorithm.