改进PSO算法在主汽温系统PID参数优化中的应用

Application of Improved Particle Swarm Optimization Algorithm in PID Controller Parameters Optimization for Main Steam Temperature System

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。通过对粒子赋予不同的初始惯性权重,较好地协调了粒子的全局与局部搜索能力。通过对具有严重参数不确定性、多扰动以及大迟延的电厂主汽温被控对象的仿真研究,结果表明:改进的粒子群算法在保证PID控制稳定性基础上提高了PID控制的精度,且编码简单,易于实现,具有较好的应用前景。

The thesis proposes a PID controller parameter tuning method based on improved particle swarm optimization （PSO） algorithm. Given by different initial particle inertia weight, it can coordinate the overall situation of the particle and local search capabilities. Simulation study on power plant main steam temperature whose parameters have serious uncertainty, disturhances, as well as large delay shows that ： Based on the stability of PID control, the improved particle swarm optimization algorithm improves the accuracy of PID control, and the coding is simple, as well as easy to implement, so it has good application prospects.