基于混合优化鱼群算法的近空间飞行器控制分配

为解决近空间飞行器的控制分配问题, 研究了一种融合了差分进化与遗传进化的鱼群优化算法控制分配策略。该方法能充分考虑执行器的动态约束, 根据操纵面物理约束随机产生鱼群的初始个体, 再利用鱼群算法进行全局搜索。鱼群算法搜索范围从全局搜索快速收缩进入局部搜索, 在收敛速度减慢或停滞时, 利用差分进化算法运行速度快及局部优化的优势, 以提高收敛速度和精度。同时在差分进化未能获取更优解时, 由遗传算法进行全局寻优, 避免分配结果收敛于局部最优解, 从而提高整个分配算法效率。同时将该方法应用于某近空间飞行器。仿真结果表明, 该控制分配方法能有效地将控制指令分配到各操纵面上, 实现良好的跟踪效果。

Control Allocation Based on Hybrid Optimization Fish Swarm Algorithm for Near Space Vehicles

According to the control allocation problem of near space vehicles, a control allocation method based on HOFSA（ Hybrid Optimization Fish Swarm Algorithm） is studied. HOFSA fused by differential evolution and genetic evolution is applied to dynamic constrained control allocation problem by fully considering the dynamic constraints. The initial fishes are generated randomly according to the constraints of the control surfaces, and the fish swarm algorithm is used to achieve a global search. The fish swarm algorithm can converge rapidly in the initial stage, but the convergence speed will be slow down or keep stagnant when search local region. Due to its advantage in local optimization, the differential evolution algorithm is further employed to optimize for improving convergence speed and accuracy. And under the circumstance that differential evolution has not been able to get more optimal solution, the genetic algorithm is used to avoid the local optimal solution and to improve the efficiency of the whole allocation algorithm. The developed control allocation method based on HOFSA is applied to a near space vehicle and the simulation results show that it can effectively assign control instructions to each control surface and the tacking control performance is good.