Fuzzy Approximate Learning-Based Sliding Mode Control for Deploying Tethered Space Robot

Abstract

This article proposes a hybrid control scheme synthesizing fuzzy approximate Q-iteration algorithm and discrete-time terminal-like sliding mode control for deploying tethered space robot, which is modeled as a deterministic Markov decision process. The existence of a switching condition allows FQ-iteration algorithm and terminal-like sliding surface constituting an optimal sliding mode control, and the fuzzy logic approximation is employed to improve the efficiency of optimization. Under arbitrary switching, the sliding mode reaching law works to compress the contraction of sliding surface variable. Simulation results verify the analyses on contraction of fuzzy approximate Q-iteration for optimal sliding mode control, the stability of reduced-order system yielded by the proposed discrete-time terminal-like sliding surface, and existence of switching condition.