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Adaptive tracking control of robot manipulators with input saturation and time-varying output constraints
Asian Journal of Control ( IF 2.4 ) Pub Date : 2020-03-13 , DOI: 10.1002/asjc.2305
Yuxiang Wu 1 , Rui Huang 1 , Yu Wang 1 , Jiaqing Wang 2
Affiliation  

This paper investigates adaptive tracking control in task space for robot manipulators with uncertain system dynamics, input saturation, and time-varying output constraints simultaneously. An auxiliary system is constructed to compensate the effect of the input saturation, and an asymmetric barrier Lyapunov function (BLF) is applied to tackle time-varying output constraints, while radial basis function (RBF) neural networks (NN) are used to approximate the unknown closed-loop dynamics. By introducing a disturbance observer (DO), unknown external disturbances from humans and environment are estimated, and NN approximation errors are compensated. A novel adaptive NN tracking controller is designed to guarantee all signals in the closed-loop system are semi-globally uniformly ultimately bounded (UUB), while the tracking errors and observer errors converge to a small neighborhood of zero, and the time-varying output constraints are not violated. Moreover, the adaptive tracking control of redundant robot manipulators is studied, and the subtask and task space tracking of redundant robot manipulators are realized simultaneously, while the stability of the system is proved by Lyapunov stability theory. Finally, some simulation results are presented to verify the effectiveness and superiority of the proposed control scheme.

中文翻译:

具有输入饱和和时变输出约束的机器人机械手自适应跟踪控制

本文同时研究了具有不确定系统动力学、输入饱和和时变输出约束的机器人机械手在任务空间中的自适应跟踪控制。构建了一个辅助系统来补偿输入饱和的影响,并应用非对称势垒李雅普诺夫函数 (BLF) 来解决随时间变化的输出约束,而径向基函数 (RBF) 神经网络 (NN) 用于逼近未知的闭环动力学。通过引入干扰观测器 (DO),估计来自人类和环境的未知外部干扰,并补偿 NN 近似误差。一种新颖的自适应神经网络跟踪控制器旨在保证闭环系统中的所有信号都是半全局一致最终有界(UUB),而跟踪误差和观察者误差收敛到零的一个小邻域,并且不违反时变输出约束。此外,研究了冗余机械臂的自适应跟踪控制,同时实现了冗余机械臂的子任务和任务空间跟踪,并通过李雅普诺夫稳定性理论证明了系统的稳定性。最后,给出了一些仿真结果来验证所提出的控制方案的有效性和优越性。Lyapunov稳定性理论证明了系统的稳定性。最后,给出了一些仿真结果来验证所提出的控制方案的有效性和优越性。Lyapunov稳定性理论证明了系统的稳定性。最后,给出了一些仿真结果来验证所提出的控制方案的有效性和优越性。
更新日期:2020-03-13
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