基于改进人群搜索算法的PID控制器参数优化  

Optomization of PID Controller Parameters Based on Improved Seeker Optimization Algorithm

作　　者：赵广元 王超 ZHAO Guang-yuan;WANG Chao(School of Automation,Xi'an University of Posts and Telecommunications,Xi'an Shanxi 710121,China;School of Information Engineering,Chang'an University,Xi'an Shanxi 710064,China)

机构地区：[1]西安邮电大学自动化学院,陕西西安710121 [2]长安大学信息工程学院,陕西西安710064

出　　处：《计算机仿真》2020年第8期302-305,310,共5页Computer Simulation

基　　金：国家自然科学基金资助项目(61503082);陕西省自然科学基金资助项目(2016JM8034);西安市科技计划项目(CXY1516(5));西安邮电大学研究生创新基金资助项目(CXJJ2017021)。

摘　　要：针对传统人群搜索算法(SOA)在搜索后期速度减缓,易造成局部优化的缺点,引入了免疫遗传算法(IGA)的选择策略。通过选择适应度值指标对个体进行评价,以PID控制器的三个参数为搜索对象,经过多次迭代寻优,获取PID控制器的最优控制参数,加快了搜索速度,提高了算法的全局搜索能力。将改进后的人群搜索算法应用于PID控制器参数优化,对比单纯的人群搜索算法和免疫遗传算法。软件仿真表明,改进后的SOA具有更好的优化效果,改善了系统性能,验证了其优越性和有效性。Aiming at flaws of traditional seeker optimization algorithm,such as slowing down in the later period of search and easy to fall into the local extremum,a selection strategy of immune genetic algorithm was introduced in the paper.Individuals were evaluated by selecting fitness value indicators,and the three parameters of PID were taken as a search target.After many iterations of optimization,we obtained the optimal control parameters of the PID controller,speeded up the searching and improved the global searching ability of the algorithm.The improved seeker optimization algorithm was used for PID controller parameters tuning optimization and compared with the traditional seeker optimization algorithm and immune genetic algorithm.The simulation results show that improved seeker optimization algorithm has better effects and enhances the system performance,which verifies its superiority and effectiveness.

关 键 词：人群搜索算法 免疫遗传算法 选择策略 适应度值 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]