This paper presents an improved particle swarm optimization (PSO) algorithm to optimize the coefficients of the proportional integral derivative (PID) controller for trajectory tracking accuracy problems. Nonlinear adaptive methods are adopted on the inertia weight to built a balance between the local and global search areas, asynchronous change strategies are introduced into the learning coefficients to rationally use individual and group experience, an elite mutation method is designed to mutate the global best position in each iteration. Basic PSO (BPSO-PID) and Ziegler-Nichols PID controller (ZN-PID) are implemented with step, sinusoidal and slope references on a co-simulation platform. The simulation results illustrate that high accuracy and fast convergence can be obtained based on the improved PSO algorithm PID controller (IPSO-PID). Experimental results demonstrated that the IPSO-PID method can achieve the highest tracking accuracy, compared with the BPSO-PID method, this method can improve the tracking accuracy by 37.14% and 50.32%, respectively.

本文提出了一种改进的粒子群算法（PSO），用于针对轨迹跟踪精度问题优化比例积分微分（PID）控制器的系数。在惯性权重上采用非线性自适应方法，在局部和全局搜索区域之间建立平衡，在学习系数中引入异步更改策略，以合理利用个人和小组的经验，设计一种精英变异方法来变异全局最佳位置在每次迭代中。基本PSO（BPSO-PID）和Ziegler-Nichols PID控制器（ZN-PID）在联合仿真平台上通过阶跃，正弦和斜率参考实现。仿真结果表明，基于改进的PSO算法PID控制器（IPSO-PID）可以实现高精度和快速收敛。