基于混合粒子群算法的PID參數(shù)尋優(yōu)

提出一種將單純形法SM與粒子群算法PSO結(jié)合的混合粒子群算法HPSO。通過對3種常用測試函數(shù)進(jìn)行優(yōu)化和比較，結(jié)果表明HPSO比PSO和SM都更容易找到全局最優(yōu)解。然后用HPSO優(yōu)化算法對某渦扇發(fā)動(dòng)機(jī)PID控制中的參數(shù)進(jìn)行優(yōu)化并將結(jié)果與混合遺傳算法HGA的結(jié)果進(jìn)行比較，結(jié)果表明HPSO在找尋最優(yōu)解效率上好于HGA。且算法實(shí)現(xiàn)簡單,具有很高的可靠性,是一種PID控制參數(shù)尋優(yōu)的有效方法。
關(guān)鍵詞: 粒子群優(yōu)化算法; 單純形算法; 航空發(fā)動(dòng)機(jī); PID控制; 遺傳算法

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;