Motion control of a space manipulator using fuzzy sliding mode control with reinforcement learning

Abstract

The free-flying space manipulators present challenges in controlling the motions of both the spacecraft bus and the manipulator, because of the highly-coupling system dynamics and the unknown space environment disturbances. Although the sliding mode controllers are robust to the unknown disturbances and system uncertainties, the chattering effect could affect the pointing accuracy and the lifetime of the actuators. This paper first introduces the dynamics of a CuBot, which is a 3-rigid-link manipulator based on the CubeSat platform. To maintain the robustness while decreasing the chattering effect, an innovative reinforcement learning based fuzzy adaptive sliding mode controller is proposed. To maintain the robustness while reducing the chattering effect, an innovative reinforcement learning based fuzzy adaptive sliding mode controller is proposed. The switching gain is updated to estimate the lumped upper bound of the system uncertainties and the unknown disturbances, and then a new fuzzy logic adaptive law is applied on the switching gain to decrease the chattering effects. On top of that, the fuzzy logic rules are tuned by an innovative modified reinforcement learning mechanism to achieve the better tracking performance. The uniformly ultimately bounded tracking errors are guaranteed by the proposed control scheme, and the effectiveness is validated by the simulation results.