Prescribed-time adaptive event-triggered control for robot manipulators based on command filtering.

Yongling Xia, Yanbin Liu, Weichao Sun
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Abstract

This article investigates the prescribed-time tracking control of uncertain manipulator systems with external disturbances. Combining with command filtering and neural network techniques, a novel prescribed-time adaptive event-triggered control scheme is proposed, where neural networks are utilized to handle manipulators model uncertainties. Based on a piecewise function, a sufficient condition for prescribed-time stability is provided, which decouples the convergence domain and the settling time into separately preset parameters. The proposed approach not only avoid the "explosion of complexity" problem, but also deal with filter errors by using an error compensation strategy. In addition, an adaptive estimation strategy is developed to compensate for external disturbances, and an event trigger mechanism is introduced to save system communication resources. Finally, the superiority of our proposed prescribed-time control approach is demonstrated via simulation results.

基于命令滤波的机器人操作臂定时自适应事件触发控制。
研究了具有外部扰动的不确定机械臂系统的定时跟踪控制问题。将指令滤波技术与神经网络技术相结合,提出了一种新的规定时间自适应事件触发控制方案,利用神经网络处理机械手模型的不确定性。基于分段函数,给出了给定时间稳定性的充分条件,将收敛域和稳定时间解耦为单独的预设参数。该方法不仅避免了“复杂度爆炸”问题,而且采用误差补偿策略处理滤波误差。此外,开发了自适应估计策略以补偿外部干扰,并引入了事件触发机制以节省系统通信资源。最后,通过仿真结果验证了所提出的规定时间控制方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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