Disturbance Observer-Based Prescribed Performance Predictive Control for Spacecraft On-Orbit Inspection
Jiangliang Fu, Li Chen, Dexin Zhang, Jun Zhang, Xiaowei Shao
Abstract
Spacecraft on-orbit inspection provides a prerequisite for the subsequent autonomous proximity operations. To fulfill the mission, robust constrained control algorithms for relative position tracking and attitude adjustment should be employed to handle the nonlinear coupled dynamics, system constraints, and external disturbances. In this paper, a robust nonlinear model predictive control (NMPC) scheme with prescribed performance based on a fixed-time neural network disturbance observer is proposed. Predefined tracking performance requirements are achieved, and, meanwhile, safety and stability are guaranteed. By exploiting the prescribed performance control technique, the proposed NMPC structure is capable of establishing a quantitative relationship between design parameters and certain prespecified performance values. Moreover, the proposed fixed-time neural network disturbance observer shows superiority in the disturbance estimations due to the property of universal approximation and fixed-time convergence. To illustrate the capabilities of our algorithm, comparison simulation tests concerning with prescribed performance and disturbance estimating are presented. Selection of tuning parameters is discussed, and computational load of the proposed algorithm is profiled.