多目标遗传算法在电机控制系统参数整定中的应用

本文给出了一种基于多目标遗传算法的PID参数设计方法，综合考虑系统超调量、稳定时间和ITAE指标，采用多目标遗传算法（NSGAⅡ）求出Pareto最优解。使用信息熵法对最优解的属性进行权值计算，根据逼近理想解的排序方法对Pareto最优解给出排序。计算了一个电机控制的数值算例，结果表明本文方法所设计的PID控制器性能优异，适合工程实际应用。

Multiobjective Optimization and Multi-attribute Decision Making for PID Parameters Tuning in Motor Controller System

The tuning of PID controller parameters is the most important task in PID design process. A new tuning method is presented for PID parameters based on multiobjective optimization technique. A Non-dominated Sorting Genetic Algorithm Ⅱ （NSGA Ⅱ） is employed to approximate the set of Pareto solution through an evolutionary optimization process. And a multi-attribute decision making approach is adopted to rank these solutions from best to worst and to determine the best solutlon in a deterministic environment with a single decision maker A PID design example for motor is conducted to illustrate the analysis process in present study. The ranking of Pareto solution is based on entropy weight and TOPSIS method.