A model reference adaptive control scheme for pure-feedback nonlinear systems
Kwanghee Nam,Aristotle Arapostathis +1 more
- Iss: 24, pp 577-582
TLDR
In this article, a model reference adaptive control scheme for nonlinear systems in a pure-feedback canonical form with unknown parameters is presented, where the presence of parameter uncertainty in the system causes imperfect linearization, i.e., it introduces nonlinear additive terms in the transformed coordinates.Abstract:
We present a model reference adaptive control scheme for nonlinear systems in a pure-feedback canonical form with unknown parameters. The presence of parameter uncertainty in the system causes imperfect linearization, i.e., it introduces nonlinear additive terms in the transformed coordinates. Provided that these nonlinear terms are dominated by the norm of the transformed state, we establish global convergence of the output error for all initial estimates of the parameter vector lying in an open neighborhood of the true parameters in the parameter space.read more
Citations
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Proceedings ArticleDOI
Systematic Design of Adaptive Controllers for Feedback Linearizable Systems
TL;DR: In this paper, a systematic procedure is developed for the design of adaptive regulation and tracking schemes for a class of feedback linearizable nonlinear systems, which are transformable into the so-called pure-feedback form.
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Neural Network Control of Robot Manipulators and Nonlinear Systems
TL;DR: This graduate text provides an authoritative account of neural network (NN) controllers for robotics and nonlinear systems and gives the first textbook treatment of a general and streamlined design procedure for NN controllers.
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Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form
TL;DR: A backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty is developed and is able to eliminate the problem of "explosion of complexity" inherent in the existing method.
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Adaptive nonlinear regulation: estimation from the Lyapunov equation
TL;DR: In this paper, a stabilizing adaptive controller for a nonlinear system depending affinely on some unknown parameters is presented, where the adaptive law is designed using the Lyapunov equation.
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Adaptive neural control of uncertain MIMO nonlinear systems
Shuzhi Sam Ge,Cong Wang +1 more
TL;DR: Adapt neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms that avoid the controller singularity problem completely without using projection algorithms.
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