改进遗传算法整定自抗扰控制器参数及应用

非线性自抗扰控制器策略(ADRC)耦合参数众多,单一机制优化算法在进行参数整定时易陷入局部最优解,极大地降低了自抗扰控制器的控制精度。针对此问题,该文提出了一种改进遗传算法(KFC-2PMGA)进行自抗扰控制器参数整定,将核模糊聚类算法应用到遗传算法双种群划分中,并针对聚类所得双种群,分别采用不同的自适应交叉及变异策略,有效地避免了传统遗传算法易产生"早熟"的现象。并以永磁同步电机为例进行仿真验证。结果表明,优化后的自抗扰控制器具有良好的系统响应及控制精度,改进后的遗传优化算法适用于复杂非线性自抗扰控制器参数整定。

Parameters Optimization and Application of Auto-disturbance-rejection-controller Based on Improved Genetic Algorithm

The nonlinear auto disturbance rejection controller strategy(ADRC) has many coupling parameters. The single mechanism optimization algorithm is easy to fall into the local optimal solution when performing parameter tuning,which greatly reduces the control precision of the auto disturbance rejection controller. Aiming at this problem,this paper proposes an improved genetic algorithm(KFC-2 PMGA) for the auto-disturbance controller parameter tuning,and applies the nuclear fuzzy clustering algorithm to the genetic algorithm dual population partitioning,and for the clustered double population,respectively Different adaptive crossover and mutation strategies are adopted to effectively avoid the phenomenon that traditional genetic algorithms are prone to "premature". The permanent magnet synchronous motor is taken as an example to verify the simulation. The results show that the optimized auto-disturbance rejection controller has good system response and control precision,and the improved genetic optimization algorithm.Suitable for parameter setting of complex nonlinear auto disturbance rejection controllers.