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neural adaptive chaotic control with constrained input using state and output feedback

Chinese Physics B 2015年第1期24卷 170-176页

作者：高士根董海荣孙绪彬宁滨 State Key Laboratory of Rail Traffic control and Safety Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University

This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks（RBF-NNs） are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode（HOSM） observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes.

关键词： chaotic control neural adaptive control constrained input

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neural adaptive Dynamic Surface control for Mismatched Uncertain Nonlinear Systems with Nonlinear Feedback Errors

Neural Adaptive Dynamic Surface Control for Mismatched Uncer...

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第36届中国控制会议

作者： Shigen Gao Hairong Dong Bin Ning Tao Tang Yidong Li Kimon P.Valavanis State Key Laboratory of Rail Traffic control and Safety Beijing Jiaotong University School of Computer and Information Technology Beijing Jiaotong University Department of Electrical and Computer Engineering School of Engineering and Computer ScienceUniversity of Denver

ISBN: (纸本)9781538629185

This paper addresses a new nonlinear gain feedback based neural adaptive dynamic surface control（DSC） method for a class of strict feedback nonlinear systems in the presence of uncertainties and external disturbances. Besides circumventing the problem of complexity explosion, the nonlinear gain feedback technique brings a self-regulation ability into the pioneering DSC method to improve the dynamic performance of the resulted closed-loop system. neural networks are used to online approximating the uncertainties, the over-fitting problem occurs with small probability since the signals obtained by NNs are also handled using nonlinear gain function. It is proved rigorously by Lyapunov stability theorem that all the closed-loop signals are kept semi-globally uniformly ultimately bounded, the output tracking error and the optimal neural weight vector estimation error can all be adjusted to arbitrarily small around zero characterized by design parameters in an explicit way. Furthermore,the method proposed can be extended to the control of multiple-input-multiple-output system trivially. Comparative simulation results are presented to demonstrate the effectiveness of the proposed method.

关键词： Dynamic Surface control neural adaptive control Nonlinear Gain Feedback

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adaptive Cooperative Tracking control for A Class of High-Order Nonaffine Nonlinear Multi-Agent Systems

Adaptive Cooperative Tracking Control for A Class of High-Or...

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第30届中国控制与决策会议

作者： Anguo Zhang Yongcheng Zhou Qing Chen Shiyin Gong Research Institute of Ruijie Ruijie Networks Co.Ltd State Key Laboratory of Advanced Power Semiconductor Devices Zhuzhou CRRC Times Electric Co. Ltd College of Automation Chongqing University Hunan Railway Professional Technology Institute of Science and Technology

This paper investigated the adaptive cooperative tracking control for a class of high-order nonaffine nonlinear multi-agent *** effective distributed adaptive control strategy was proposed only based on the local state information of the network neighbor *** main contribution was a novel dynamics transformation that converted the nonaffine system into an affine system through the mean value *** Basis Function neural Networks（RBFNNs） were used to approximate the unknown nonlinear function of the multi-agent systems,and a Lyapunov function was designed for the robust adaptive synchronization control *** the end,simulation results demonstrated the effectiveness of the proposed control method.

关键词： Multi-Agent System Nonaffine Nonlinear System Cooperative control neural adaptive control

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