Space manipulator optimal impedance control using integral reinforcement learning

Abstract

This paper examines the optimal impedance control problem for large-scale space manipulator operational tasks with unknown contact dynamics and partial measurements. More specifically, by quantifying the interaction performance using a discounted value function, the optimal impedance control problem is tactfully transformed into a linear quadratic tracking problem. By resorting to the historical inputs and outputs, an improved state reconstruction method is presented, which obviates the velocity measurement. Unlike the existing state reconstruction method for continuous-time systems, the estimation bias caused by probing noise is completely eliminated under the improved state reconstruction method. Based on this, a novel model-free value iteration integral reinforcement learning algorithm is developed to approximate optimal impedance parameters. Compared with the earlier integral reinforcement learning algorithms, the proposed algorithm not only averts any prior contact dynamics knowledge and full-state measurements, but also eliminates the heavy dependence on the specific initial stabilization control. In addition, the implementation and convergence of the proposed algorithm are discussed successively. Finally, numerical simulations verify the effectiveness of the theoretical results.