Robust Cascade Path-Tracking Control of Networked Industrial Robot Using Constrained Iterative Feedback Tuning

Abstract

Industrial robots can be found in many manufacturing applications that suffer from imprecise position control of their own drive systems due to unknown external disturbances and parametric uncertainties. To address this problem, this paper proposes a robust cascade path-tracking control method to achieve better position control performance for a networked industrial robot. In the joint task space, the cascade control framework is formulated for the developed robotic actuation system, which consists of an inner speed loop and an outer position loop. Instead of exploring the conventional model-based approaches, a multiple degree-of-freedom constrained iterative feedback tuning (CIFT) method is presented to regulate the cascade controller by utilizing the monitored process data straightforwardly. With the integration of the normalized input constraints and position tracking error, the proposed CIFT method seeks an optimal solution to track the desired position profiles with satisfactory accuracy and improved robustness. Theoretical analysis is performed to verify the asymptotical convergence of the closed-loop system. Implemented on a real-time networked industrial robot, experimental results demonstrate that the proposed method can enhance the dynamic path tracking and system robustness during various operating situations.