双种群教与学算法在磁轴承PID控制器中的应用
发布时间：2018-08-12 20:59
【摘要】：针对主动磁轴承系统的开环不稳定性,提出采用改进双种群教与学算法与PID结合的方法实现对系统的闭环控制。该算法在教与学算法对PID参数的优化中加入教师反向学习和学生变异学习,解决基本算法的早熟问题,提高解的精度。运用MATLAB/Simulink搭建系统模型进行仿真试验,结果表明:与基本教与学算法和遗传算法相比,该算法收敛到最优参数值的速度更快,PID控制磁轴承系统的响应速度更快,具有更好的动态性能和稳态性能。
[Abstract]:In view of the open-loop instability of active magnetic bearing system, an improved dual-population teaching and learning algorithm combined with PID is proposed to realize the closed-loop control of the system. In order to solve the precocious problem of the basic algorithm and improve the accuracy of the solution, teachers' reverse learning and students' mutation learning are added to the optimization of PID parameters in the algorithm. The simulation results show that compared with the basic teaching and learning algorithm and genetic algorithm, the algorithm converges to the optimal parameter value faster and the response speed of the pid control magnetic bearing system is faster than that of the basic teaching and learning algorithm and the genetic algorithm. It has better dynamic and steady performance.
【作者单位】： 南通大学电气工程学院;常州工学院电子信息与电气工程学院;
【基金】：国家自然科学基金项目(51175052)
【分类号】：TH133.3
[Abstract]:In view of the open-loop instability of active magnetic bearing system, an improved dual-population teaching and learning algorithm combined with PID is proposed to realize the closed-loop control of the system. In order to solve the precocious problem of the basic algorithm and improve the accuracy of the solution, teachers' reverse learning and students' mutation learning are added to the optimization of PID parameters in the algorithm. The simulation results show that compared with the basic teaching and learning algorithm and genetic algorithm, the algorithm converges to the optimal parameter value faster and the response speed of the pid control magnetic bearing system is faster than that of the basic teaching and learning algorithm and the genetic algorithm. It has better dynamic and steady performance.
【作者单位】： 南通大学电气工程学院;常州工学院电子信息与电气工程学院;
【基金】：国家自然科学基金项目(51175052)
【分类号】：TH133.3
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