Journal ArticleDOI
Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints
TLDR
The auxiliary design system is introduced to analyze the effect of input constraints, and its states are used to adaptive tracking control design, and the closed-loop semi-global uniformly ultimate bounded stability is achieved via Lyapunov synthesis.About:
This article is published in Automatica.The article was published on 2011-03-01. It has received 861 citations till now. The article focuses on the topics: Adaptive control & Nonlinear control.read more
Citations
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Journal ArticleDOI
Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints
Wei He,Yuhao Chen,Zhao Yin +2 more
TL;DR: Adaptive neural network control for the robotic system with full-state constraints is designed, and the adaptive NNs are adopted to handle system uncertainties and disturbances.
Journal ArticleDOI
Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation
Wei He,Yiting Dong,Changyin Sun +2 more
TL;DR: In this article, an adaptive impedance controller for a robotic manipulator with input saturation was developed by employing neural networks. But the adaptive impedance control was not considered in the tracking control design, and the input saturation is handled by designing an auxiliary system.
Journal ArticleDOI
Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints
Yan-Jun Liu,Shaocheng Tong +1 more
TL;DR: An adaptive control technique is developed for a class of uncertain nonlinear parametric systems and it is proved that all the signals in the closed-loop system are global uniformly bounded and the tracking error is remained in a bounded compact set.
Journal ArticleDOI
Adaptive output-feedback control design with prescribed performance for switched nonlinear systems
TL;DR: An output feedback control method with prescribed performance is proposed for single-input and single-output (SISO) switched non-strict-feedback nonlinear systems and it is shown that all the signals in the resulting closed-loop system are semi-globally uniformly ultimately bounded.
Journal ArticleDOI
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
Wei He,Yiting Dong +1 more
TL;DR: With the proposed control, the stability of the closed-loop system is achieved via Lyapunov’s stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty.
References
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Book
Stable Adaptive Systems
TL;DR: Stability theory simple adaptive systems adaptive observers the control problem persistent excitation error models robust adaptive controlThe control problem - relaxation of assumptions multivariable adaptive systems applications of adaptive control.
Journal ArticleDOI
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.
Journal ArticleDOI
A robust adaptive nonlinear control design
TL;DR: The overall adaptive scheme is shown to guarantee global uniform ultimate boundedness.
Journal ArticleDOI
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.
Journal ArticleDOI
Brief paper: Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form
Tao Zhang,Shuzhi Sam Ge +1 more
TL;DR: It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants.