Force tracking impedance control with unknown environment via an iterative learning algorithm

Abstract

Within impedance control framework, since force tracking accuracy is affected by the robotic tracking error, an adaptive force control scheme combined with an iterative learning algorithm is proposed in this paper. First, the unknown environment stiffness and location are observed in real time based on the actual contact force and robot position, and the adaptive reference trajectory is obtained by the estimated environment information. Then, position-based impedance control scheme is adopted to adjust the dynamical relationship between the end-effect or position and the contact force. Considering the effect of the position tracking error on the force tracking accuracy, an iterative learning control method is utilized to compensate the motion error effectively by the force error. The proposed method can effectively reduce the steady state force error by simply combining the indirectly adaptive reference trajectory generation technique with position tracking error regulation method. Stability and convergence conditions are presented with a Lyapunov function and frequency domain method. Simulations and experiments are conducted on a biaxial platform to demonstrate the effectiveness of the proposed method.