基于改进PSO的飞行控制律参数多目标优化方法

针对传统飞行控制律参数单目标优化设计不能同时满足多控制指标要求,且与飞行品质要求缺乏相关性,以及物理意义不明确等缺点,提出了一种基于改进粒子群算法的飞行控制律多目标优化设计方法。该算法模拟鸟类捕食过程,使得种群随着"食物"的发现和消耗,聚集为数量和构成动态调整多个子群,且子群粒子速度也随之进行自适应变异。从而,有利于维持种群的多样性,有效抑制早熟收敛现象发生。最后,还使用改进的粒子群优化算法对某型飞机横侧向控制律设计进行了数值仿真,结果显示该算法有效提高控制律优化调参效率,可满足期望的飞行品质要求。

Multi-objective Optimization of Parameters for Flight Control Law Based on PSO

In the traditional optimization design of flight control system（FCS）,there are some disadvantages such as weak correlation between the single object and the flight quality requirements,ambiguous physical meaning and difficulty of using single object to optimize many objects at the same time.To solve such problem,an improved particle swarm optimization（PSO） algorithm was proposed. By simulating the foraging aggregation behavior of birds, the particles can be divided into several dynamic sub-swarms with respect to the finding and expanding of ＂forage＂ in the improved PSO algorithm. Hence, the diversity of particles can be maintained by this method, and local optimum phenomena can be restrained. Finally,using the improved PSO algorithm for numerical simulation of lateral control law of a certain type of aircraft,the results show that the proposed algorithm can effectively improve the efficiency of the FCS parameters tuning,and the results can meet the flight quality requirements.