Adaptive reactionless control strategy via the PSO-ELM algorithm for free-floating space robots during manipulation of unknown objects

The operation of free-floating space robots causes significant challenges to the attitude control of a spacecraft body, resulting from the strong dynamic coupling existing in the system as well as the appending unknown property from a captured object. Conventional reactionless motion planning and control methods require the precise knowledge of system dynamics with ideal path tracking performance, which are impossible to achieve in reality. To solve this problem, an adaptive reactionless path planning and control integrated strategy is proposed for free-floating space robots while manipulating unknown objects. For details, a SW-RLS (Slide-windowed Recursive Least Square) algorithm is employed to construct the adaptive reactionless path planning by identifying and compensating the unknown property in the form of a time-variable matrix for the momentum equation of the system. Simultaneously, a robust adaptive control strategy via the PSO-ELM (Particle Swarm Optimization-Extreme Learning Machine) algorithm is proposed to track the dynamic change in the planned path, based on the foundation of a designed PD controller for reducing the error. With computational efficiency, an adaptive controller via PSO-ELM aims at learning and compensating for the unknown property intelligently; then, a robust controller is used to reject the model uncertainties. The notable feature of the proposed strategy is that it requires neither an accurate system model nor any information about the unknown property. Most importantly, the design can dynamically realize reactionless path tracking performance. Finally, the simulations of a free-floating space robot capturing an unknown object and comparisons with existing methods are presented to demonstrate the effectiveness of the proposed scheme.