【作者】 栾丽君； 谭立静； 牛奔；

【Author】 LUAN Li-jun, TAN Li-jing,NIU Ben (School of Mechanical Engineering, Liaoning Technical University, Fuxin 123000, China;Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016, China.

【机构】 辽宁工程技术大学机械工程学院； 中国科学院沈阳自动化研究所；

【摘要】 PID控制器的参数整定,从优化角度就是在Kp,Ki,Kd3个参数空间中寻找最优值,使系统的控制性能达到最优．粒子群优化(PSO)算法是一种新兴的演化算法,该算法与传统方法相比有着较高的收敛速度和计算精度．对此,提出一种基于粒子群算法的PID控制参数自整定方法．在实验仿真中与基于二进制和十进制遗传算法的PID参数整定方法进行了比较．结果表明,本算法优于这两种遗传算法,可快速有效地实现PID控制器参数的自整定．更多还原

【Abstract】 As viewed from optimization, the self-tuning of PID controller parameters is to find the best global optimum in the solution space with Kp,Ki,Kd . Particle swarm optimization (PSO) is a new evolutionary computation, which has a better convergence rate and computation precision compared with other evolutionary algorithms. A novel design scheme for self-tuning PID controller parameters by using the PSO algorithm is presented. It describes in detail how to employ PSO to determine PID gains so that optimal control can be maintained. In the simulation part the proposed method is compared with binary coded GA and real coded GA. The experimental results illustrate that PSO performs better than GAs in terms of the convergence rate and solution precision.更多还原