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Adaptive neural network based prescribed performance control for teleoperation system under input saturation

机译:输入饱和下基于自适应神经网络的远程操作系统规定性能控制

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摘要

This paper addresses the stability and position synchronization problems for bilateral nonlinear teleoperation system with asymmetric constant time delays under input saturation. Compared with previous work, not only the steady-state performance but also the transient-state performance is considered. In the presence of system uncertainties and external disturbances, the corresponding adaptive neural network (ANN) based prescribed performance control (PPC) scheme is designed. Moreover, the time-dependent stability conditions are derived by applying the linear matrix inequality (LMI). Finally, in simulation comparisons with P+d (proportion plus differential) controller are conducted, simulation results are presented to demonstrate the effectiveness of the proposed PPC approach. (C) 2015 The Franldin Institute. Published by Elsevier Ltd. All rights reserved.
机译:本文研究了在输入饱和下具有非对称恒定时滞的双边非线性遥操作系统的稳定性和位置同步问题。与以前的工作相比,不仅要考虑稳态性能,还要考虑瞬态性能。在存在系统不确定性和外部干扰的情况下,设计了基于相应的自适应神经网络(ANN)的规定性能控制(PPC)方案。此外,通过应用线性矩阵不等式(LMI)导出了随时间变化的稳定性条件。最后,在与P + d(比例加微分)控制器的仿真比较中,给出了仿真结果以证明所提出的PPC方法的有效性。 (C)2015弗兰丁研究所。由Elsevier Ltd.出版。保留所有权利。

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  • 来源
    《Journal of the Franklin Institute》 |2015年第5期|1850-1866|共17页
  • 作者单位

    Yanshan Univ, Inst Elect Engn, Qinhuangdao 066004, Peoples R China;

    Hebei United Univ, Inst Informat Engn, Tangshan 063009, Peoples R China;

    Hebei United Univ, Inst Informat Engn, Tangshan 063009, Peoples R China;

    Hebei United Univ, Inst Informat Engn, Tangshan 063009, Peoples R China;

    Yanshan Univ, Inst Elect Engn, Qinhuangdao 066004, Peoples R China;

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