基于免疫粒子群算法的飞行控制器参数寻优

提出了一种免疫粒子群混合优化算法.该方法将免疫算法中的基于浓度的抗体繁殖策略与粒子群优化算法相结合.对浓度低的粒子进行促进,对浓度高的粒子进行抑制,因而保持了粒子的多样性,克服了PSO算法易于陷入局部最优点的缺点,寻优速度快.将该方法用于飞行控制器的参数优化设计.仿真结果表明：使用该方法进行参数优化设计获得了优良的飞行控制效果,能够较大地提高飞行控制器参数的设计效率.

Optimization of Flight Controller Parameters Based on PSO-Immune Algorithm

A PSO-lmmune algorithm was proposed. In this method, the reproduction strategy based on density of immune algorithm was connected with the PSO algorithm to promote the particle whose density is low and to limit the particle whose density is high. In that way, the multiplicity of the particles is maintained and trapping in local minimum is avoided. The flight controller parameters were optimized using this method. The simulation results show that the good flight control performance is obtained compared with traditional trail-and-error method and the design efficiency is increased.