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Journal ArticleDOI

Adaptive neural control for a class of stochastic nonlinear systems by backstepping approach

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TLDR
This paper addresses adaptive neural control for a class of stochastic nonlinear systems which are not in strict-feedback form and guarantees that all the closed-loop signals are bounded and the tracking error converges to a sufficiently small neighborhood of the origin.
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This article is published in Information Sciences.The article was published on 2016-11-10. It has received 165 citations till now. The article focuses on the topics: Stochastic neural network & Backstepping.

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Citations
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Journal ArticleDOI

Event-Triggered Adaptive Tracking Control for Multiagent Systems With Unknown Disturbances

TL;DR: It is shown that all the signals are bounded, and the consensus tracking errors are located in a small neighborhood of the origin based on the Lyapunov stability theory and backstepping approach and is proved by simulation results.
Journal ArticleDOI

Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure

TL;DR: Unlike the existing results on adaptive neural/fuzzy control, the proposed adaptive neural controller guarantees that the tracking error converges to a sufficiently small domain around the origin in finite time, and other closed-loop signals are bounded.
Journal ArticleDOI

A Novel Finite-Time Control for Nonstrict Feedback Saturated Nonlinear Systems With Tracking Error Constraint

TL;DR: This article investigates the neural network-based finite-time control issue for a class of nonstrict feedback nonlinear systems, which contain unknown smooth functions, input saturation, and error constraint.
Journal ArticleDOI

Adaptive neural networks finite-time tracking control for non-strict feedback systems via prescribed performance

TL;DR: The proposed method can guarantee that the tracking error converges to an arbitrarily small region at any settling time and all the signals in the closed-loop system are semi-globally practical finite-time stable (SGPF-stable).
References
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Book

Nonlinear and adaptive control design

TL;DR: In this paper, the focus is on adaptive nonlinear control results introduced with the new recursive design methodology -adaptive backstepping, and basic tools for nonadaptive BackStepping design with state and output feedbacks.
Book

Stochastic Stability of Differential Equations

TL;DR: In this article, the authors define the boundedness in probability and stability of Stochastic Processes Defined by Differential Equations (SDEs) defined by Markov Processes.
Reference BookDOI

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.
Journal ArticleDOI

A robust adaptive nonlinear control design

TL;DR: The overall adaptive scheme is shown to guarantee global uniform ultimate boundedness.
Journal ArticleDOI

Survey Constructive nonlinear control: a historical perspective

TL;DR: This survey describes the 'activation' of stability, optimality and uncertainty concepts into design tools and constructive procedures in nonlinear control theory and concludes with four representative applications.
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