Chinese Space Science and Technology ›› 2021, Vol. 41 ›› Issue (1): 64-74.doi: 10.16708/j.cnki.1000-758X.2021.0008

Previous Articles     Next Articles

Multi-objective robust attitude control via DPSO algorithm for flexible spacecraft 

WANG Mengfei,ZHANG Jun   

  1. 1.Beijing Institute of Control Engineering, Beijing100190, China
    2.National Laboratory of Space Intelligent Control, Beijing100190, China
  • Published:2021-02-25 Online:2021-02-02

Abstract: Attitude control system with high performance for complex spacecraft is the foundation of modern space mission, and multiple objectives such as robustness, convergence speed, accuracy and control energy are required. However, most of the current control systems are designed for a single objective. Aiming at the problem of multi-objective attitude control for large flexible spacecraft, a robust design method based on differential particle swarm optimization algorithm and output feedback was proposed. Firstly, the dynamic model with parameter uncertainty was derived. Then, the differential particle swarm optimization algorithm and the linear matrix inequality (LMI) expression of robust D-stability were given. Finally, under the regional pole constraint and Pareto optimal principle, the proposed algorithm was used to optimize the objectives about disturbance suppression and control energy. The feedback gain matrix was obtained. This method satisfies the requirement of multiobjective constraints and has certain effect on vibration suppression. In the multi-objective problem with regional pole assignment, it avoids the conservatism of the traditional LMI method, and also solves the difficulty of selecting the weighting coefficient when transforming multiple objectives into one index function. A simulation example illustrates the effectiveness of the proposed method. Compared with the traditional PID control, the steady-state error of attitude can be reduced by about 54% under disturbance.

Key words: flexible spacecraft, regional pole assignment, robust control, differential particle swarm optimization algorithm, multi-objective