基于改进粒子群算法的PID参数优化与仿真

提出了一种基于改进的粒子群优化（PSO）算法的PID控制器参数整定方法。该方法采用了PSO的惯性权值自适应调整机制和粒子种群的动态更新策略，用以加速优化算法的收敛和维持群体的多样性。与常规的PSO算法相比，该方法简单易行，更容易找到全局最优解，优化效率和性能明显提高。将该算法应用非最小相位、一阶滞后等系统的PID控制器参数的优化，能够使控制系统获得较好的动态特性和很强的鲁棒性。仿真实验表明了所提出算法的有效性和优越性。

Optimization and Simulation of PID Parameters Based on Improved Particle Swarm Algorithms

A modified particle swarm optimization(PSO)algorithm is proposed for tuning PID controller parameters.An adaptive tuning laws of inertia weight and a dynamic updating laws of particle swarms are adopted for accelerating particle converges and sustaining community diversity.Compare with some general PSO algorithm,the algorithm has greater efficiency,better performance and more advantages in many aspects.The PID control law optimized by the improved PSO algorithm can effectively improve the dynamic performance of non-minimum phase system and first-order plus dead-time processes with fast response and strong robustness.Numerical simulations show that the algorithms are effective and have excellent performance.