To achieve high-accuracy manipulation in the presence of unknown disturbances, we propose two novel efficient and robust motion control schemes for high-dimensional robot manipulators. Both controllers incorporate an unknown system dynamics estimator (USDE) to estimate disturbances without requiring acceleration signals and the inverse of inertia matrix. Then, based on the USDE framework, an adaptive-gain controller and a super-twisting sliding mode controller are designed to speed up the convergence of tracking errors and strengthen anti-perturbation ability. The former aims to enhance feedback portions through error-driven control gains, while the latter exploits finite-time convergence of discontinuous switching terms. We analyze the boundedness of control signals and the stability of the closed-loop system in theory, and conduct real hardware experiments on a robot manipulator with seven degrees of freedom (DoF). Experimental results verify the effectiveness and improved performance of the proposed controllers, and also show the feasibility of implementation on high-dimensional robots.

翻译：为实现存在未知扰动时的高精度操作，本文针对高维机器人机械臂提出了两种新型高效鲁棒运动控制方案。两种控制器均集成未知系统动力学估计器（USDE），无需加速度信号和惯性矩阵逆即可估计扰动。在此基础上，基于USDE框架分别设计了自适应增益控制器与超螺旋滑模控制器，以加速跟踪误差收敛并增强抗扰动能力。前者通过误差驱动控制增益强化反馈环节，后者利用不连续切换项的有限时间收敛特性。我们从理论上分析了控制信号的有界性与闭环系统稳定性，并在七自由度（DoF）机器人机械臂上开展了实际硬件实验。实验结果验证了所提控制器的有效性与性能提升，同时证明了其在高维机器人上实施的可行性。