Wang Xia, Xu Bin, Cheng Yixin, Wang Hai, Sun Fuchun
IEEE Trans Neural Netw Learn Syst. 2023 Oct;34(10):7567-7577. doi: 10.1109/TNNLS.2022.3144569. Epub 2023 Oct 5.
This article investigates the robust adaptive learning control for space robots with target capturing. Based on the momentum conservation theory, the impact dynamics is constructed to derive the relationship of generalized velocity in the pre-impact and post-impact phase. Considering the nonlinear dynamics with contact impact, the robust control using nonsingular terminal sliding mode (NTSM) and fast NTSM is designed to achieve the fast realization of the desired states. Furthermore, for the unknown dynamics of the combination system after capturing a target, the adaptive learning control is developed based on neural network and disturbance observer. Through the serial-parallel estimation model, the prediction error is constructed for the update of adaptive law. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation studies present the desired tracking and learning performance.
本文研究了具有目标捕获功能的空间机器人的鲁棒自适应学习控制。基于动量守恒理论，构建碰撞动力学以推导碰撞前和碰撞后阶段广义速度的关系。考虑具有接触碰撞的非线性动力学，设计了使用非奇异终端滑模（NTSM）和快速NTSM的鲁棒控制，以快速实现期望状态。此外，针对捕获目标后组合系统的未知动力学，基于神经网络和干扰观测器开发了自适应学习控制。通过串并联估计模型，构建预测误差以更新自适应律。证明了李雅普诺夫函数中涉及的系统信号是有界的，并且滑模面在有限时间内收敛。仿真研究展示了期望的跟踪和学习性能。
