摘要
提出一种粒子群(Particle Swarm Optimization,PSO)与人群搜索融合(Seeker Optimization Algorithm,SOA)的算法,将其用于优化工业控制过程中PID参数。充分利用粒子群算法突出的局部寻优能力与人群搜索算法的全局搜索能力,将两种算法结合,提高算法的收敛速度以及收敛精度。通过不同的典型测试函数,将其分别对比标准粒子群算法以及人群搜索算法,验证该融合算法具有更佳的优化效果。将该融合算法用于PID控制器参数优化,仿真结果表明,该融合算法提高了控制精度和系统响应速度,鲁棒性好,改善了控制系统性能。
An algorithm for particle swarm optimization(PSO)and seeker optimization algorithm(SOA)fusion is proposed,which is used to optimize PID parameters in industrial control process in this paper.Taking full advantage of the local search ability of the PSO and the global search ability of the SOA,the two algorithms are combined to improve the convergence speed and convergence precision of the algorithm.Through different typical test functions,the standard PSO and SOA are compared respectively to verify that the fusion algorithm has better optimization effect.The fusion algorithm is applied to the parameter optimization of PID controller.
出处
《工业控制计算机》
2019年第10期22-24,27,共4页
Industrial Control Computer
关键词
人群搜索算法
粒子群算法
PID控制
仿真
Seeker optimization algorithm(SOA)
Particle swarm optimization(PSO)
PID control
simulation
