Li Xingjia, Gu Jinan, Huang Zedong, Ji Chen, Tang Shixi
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China.
School of Information Engineering, Yancheng Teachers University, Yancheng 224002, China.
Math Biosci Eng. 2022 Aug 29;19(12):12601-12616. doi: 10.3934/mbe.2022588.
This paper addresses the robust enhancement problem in the control of robot manipulators. A new hierarchical multiloop model predictive control (MPC) scheme is proposed by combining an inverse dynamics-based feedback linearization and a nonlinear disturbance observer (NDO) based uncertainty compensation. By employing inverse dynamics-based feedback linearization, the multi-link robot manipulator was decoupled to reduce the computational burden compared with the traditional MPC method. Moreover, an NDO was introduced into the input torque signal to compensate and correct the errors from external disturbances and uncertainties, aiming to enhance the robustness of the proposed controller. The feasibility of the proposed hierarchical multiloop MPC scheme was verified and validated via simulation of a 3-DOF robot manipulator. Results demonstrate that the proposed controller provides comparative accuracy and robustness and extends the existing state-of-the-art algorithms for the trajectory tracking problem of robot manipulators with disturbances.
本文研究了机器人操纵器控制中的鲁棒增强问题。通过结合基于逆动力学的反馈线性化和基于非线性干扰观测器（NDO）的不确定性补偿，提出了一种新的分层多回路模型预测控制（MPC）方案。与传统的MPC方法相比，通过采用基于逆动力学的反馈线性化，多连杆机器人操纵器被解耦，从而减轻了计算负担。此外，将一个NDO引入输入扭矩信号，以补偿和校正外部干扰和不确定性产生的误差，旨在提高所提出控制器的鲁棒性。通过对一个三自由度机器人操纵器的仿真，验证了所提出的分层多回路MPC方案的可行性和有效性。结果表明，所提出的控制器具有相当的精度和鲁棒性，并扩展了现有的最先进算法，用于解决存在干扰的机器人操纵器轨迹跟踪问题。
