摘要
针对粒子群算法(PSO)在搜索空间范围较大时搜索能力变差,甚至出现不收敛问题,提出一种对PID控制器3个参数进行整定的带有目标性初始化粒子群的改进粒子群优化算法(PSOOI)。通过引入粒子间相似熵和参数内熵对初始化粒子种群进行调整,以获得分散性较高的初始种群,提高粒子群算法的全局搜索能力和收敛速度。最后针对典型的控制对象进行PID控制器3个参数整定,研究结果表明所提出的PSOOI控制算法具有较大搜索空间范围时的全局寻优能力和快速收敛性优点。
Aiming at the problem that particle swarm optimization（PSO） with large search space will have poor search ability, even can＇t converge, an improved particle swarm optimization algorithm based on the objective initialization of particle swarm（PSOOI） is proposed to ame the PID controller parameters. Particle similarity entropy and internal entropy are adopted to tune the initialization of particle swarm in order to get high dispersion of initial population and enhance the convergence speed and global search ability of PSO. It is demonstrated by numerical simulations on the classical objects to tune the parameters of the PID controller that the proposed OIPID algorithm has the excellent global optimization performance and convergence speed.
出处
《控制工程》
CSCD
北大核心
2016年第1期64-68,共5页
Control Engineering of China
基金
国家自然科学基金(61305031
51307089)
南通市科技计划项目(BK2014075)
关键词
粒子群优化算法
9PID控制
内熵
相似熵
Particle swarm optimization
PID controller
internal entropy
similarity entropy
