Journal ArticleDOI
Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems
TLDR
By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set.Abstract:
This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm.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
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.
Journal ArticleDOI
Adaptive control-based Barrier Lyapunov Functions for a class of stochastic nonlinear systems with full state constraints
TL;DR: It is proved that all the signals in the closed-loop system are semi-global uniformly ultimately bounded (SGUUB) in probability, the system output is driven to follow the reference signals, and all the states are ensured to remain in the predefined compact sets.
Journal ArticleDOI
Observer-Based Adaptive Backstepping Consensus Tracking Control for High-Order Nonlinear Semi-Strict-Feedback Multiagent Systems
TL;DR: An observer-based adaptive consensus tracking control strategy is developed for a class of high-order nonlinear multiagent systems, of which each follower agent is modeled in a semi-strict-feedback form.
References
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Journal ArticleDOI
Decentralized adaptive output-feedback stabilization for large-scale stochastic nonlinear systems
TL;DR: This paper presents a first result in stochastic, nonlinear, adaptive, output-feedback asymptotic stabilization, and the method of changing supply functions are adapted, for the first time, to deal with Stochastic and nonlinear inverse dynamics in the context of decentralized control.
Journal ArticleDOI
DSC Approach to Robust Adaptive Fuzzy Tracking Control for Strict-Feedback Nonlinear Systems
TL;DR: A robust adaptive tracking control approach is presented for a class of strict-feedback single-input-single-output nonlinear systems by employing radial-basis-function neural networks to account for system uncertainties.
Journal ArticleDOI
Adaptive NN control of uncertain nonlinear pure-feedback systems
Shuzhi Sam Ge,Chen Wang +1 more
TL;DR: This paper is concerned with the control of nonlinear pure-feedback systems with unknown nonlinear functions, and developed adaptive NN control schemes achieve semi-global uniform ultimate boundedness of all the signals in the closed-loop.
Journal ArticleDOI
An ISS-modular approach for adaptive neural control of pure-feedback systems
TL;DR: In this article, an adaptive neural control of a completely non-affine pure-feedback system using RBF neural networks is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem.
Brief paper An ISS-modular approach for adaptive neural control of pure-feedback systems
TL;DR: An ISS-modular approach is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem to provide an effective way for controlling non-affine non-linear systems.
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