Post-capture tracking control with fixed-time convergence for a free-flying flexible-joint space robot based on adaptive neural network

Aiming to achieve rapid and precise trajectory tracking for a free-flying flexible-joint space robot (FFSR) when capturing a space target with unknown mass, we developed a nonsingular fixed-time adaptive neural control scheme via backstepping technique. Radial basis function neural networks are employed to deal with the system uncertainties caused by the captured target and external disturbances. Two fixed-time auxiliary systems are designed to attenuate the impact of excessive initial nominal control input and to ensure the control system stability in the presence of physical constraints on the actuators. Moreover, a novel dynamic surface control technique is adopted to handle the complexity explosion generated by multiple derivatives of the virtual control signals. Theoretical analysis demonstrates that the FFSR system is semiglobally fixedtimely uniformly ultimately bounded and the tracking error can converge to a very small bound within fixed time. Finally, the simulation results verify the effectiveness of the proposed controller.