基于混合粒子群算法的PID参数寻优

提出一种将单纯形法SM与粒子群算法PSO结合的混合粒子群算法HPSO。通过对3种常用测试函数进行优化和比较，结果表明HPSO比PSO和SM都更容易找到全局最优解。然后用HPSO优化算法对某涡扇发动机PID控制中的参数进行优化并将结果与混合遗传算法HGA的结果进行比较，结果表明HPSO在找寻最优解效率上好于HGA。且算法实现简单,具有很高的可靠性,是一种PID控制参数寻优的有效方法。
关键词: 粒子群优化算法; 单纯形算法; 航空发动机; PID控制; 遗传算法

Optimization Methodology of PID Parameters for Aeroengines Based on Hybrid PSO CAO Zhi-song, PIAO Ying (School of Aerospace, Tsinghua University, Beijing 100084, China) Abstract: A hybrid particle swarm optimization algorithm (HPSO) was proposed based on PSO and simplex method (SM). HPSO, PSO and SM are used to resolve three widely used test functions’ optimization problems. Results show that HPSO has greater efficiency and better performance than PSO and SM. HPSO is used to optimize the aeroengine PID controller parameters, and the result indicates that HPSO can obtain the optimum solutions more easily than HGA. Although very easy to implement, this HPSO is an efficient way to optimize the PID controller parameters.
Key words: particle swarm optimization(PSO); simplex algorithm(SM); aeroengine; PID control;