基于改进粒子群算法的柴油机振动控制技术研究
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摘要
针对柴油机传统的PID振动控制器存在时滞性、人工整定参数困难,难以达到最佳减振效果这一问题,采用基于改进粒子群算法的控制器参数整定方法。在分析了振动控制系统之后,将控制器参数作为研究对象并引入粒子群算法对控制器的参数进行迭代寻优,在此基础上采用基于动态加速常数的粒子群算法,来提高粒子多样性同时加快粒子搜索目标的速度。仿真结果表明,对比传统人工及传统粒子群算法整定参数的方法,改进粒子群算法适应度更好,能够解决振动控制系统人工参数整定困难的问题,且优化后的控制器具有更好的控制效果。
To solve the problem that the traditional PID vibration controller of diesel engine has the problems of time delay and manual setting parameters, it is difficult to achieve the optimal damping effect, and a new controller parameter tuning method based on improved particle swarm optimization algorithm is used. After analyzing the vibration control system, taking the controller parameters as the research object and introducing the particle swarm optimization algorithm to iterative optimization of the controller parameters, a particle swarm optimization algorithm based on dynamic acceleration constant is used to improve the particle diversity and speed up the particle's searching target. The simulation results show that compared with traditional artificial and traditional PSO algorithm, the improved particle swarm optimization(PSO) algorithm has better adaptability and can solve the difficulty of manual parameter setting in vibration control system, and the optimized controller has better control effect.
To solve the problem that the traditional PID vibration controller of diesel engine has the problems of time delay and manual setting parameters, it is difficult to achieve the optimal damping effect, and a new controller parameter tuning method based on improved particle swarm optimization algorithm is used. After analyzing the vibration control system, taking the controller parameters as the research object and introducing the particle swarm optimization algorithm to iterative optimization of the controller parameters, a particle swarm optimization algorithm based on dynamic acceleration constant is used to improve the particle diversity and speed up the particle's searching target. The simulation results show that compared with traditional artificial and traditional PSO algorithm, the improved particle swarm optimization(PSO) algorithm has better adaptability and can solve the difficulty of manual parameter setting in vibration control system, and the optimized controller has better control effect.
引文
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5黄梅志.柴油机振动的半主动控制研究[D].武汉:武汉理工大学,2008
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9张学典,王富彦,秦晓飞.基于人工鱼群算法的分数阶PIλ控制器参数整定[J/OL].计算机应用研究,2019,36(3):1-8.http://kns.cnki.net/kcms/detail/51.1196.TP.20180209.1114.022.html,2018-05-07
10刘金琨.先进PID控制MATLAB仿真[M].北京:电子工业出版社,2011
2 Gonzalez-Vazquez S,Moreno-Valenzuela J.A new nonlinear PI//PID controller for quadrotor posture regulation[C].Proc o Electronics,Robotics and Automotive Mechanics Conference2010:642-647
3 Altintas G,Aydin Y.A comparison on genetic algorithm based integer order and fractional order PID control of magnetic bearing system[C].Proc of the IEEE International Conference on Mechatronics.2017:20-24
4任伟建,陶琳.基于粒子群算法的抽油机故障诊断研究[J].系统仿真学报,2012,24(2):482-487+492
5黄梅志.柴油机振动的半主动控制研究[D].武汉:武汉理工大学,2008
6黄伟,徐建,朱大勇,等.基于粒子群优化的动力设备主动振动控制研究[J].合肥工业大学学报(自然科学版),2016,39(4):494-498
7 Mohanty A,Viswavandya M,Mohanty S,et al.An optimised FOPID controller for dynamic voltage stability and reactive power management in a stand-alone micro grid[J].International Journal of Electrical Power&Energy Systems,2016,78:524-536
8魏秀业.基于粒子群优化的齿轮箱智能故障诊断研究[D].太原:中北大学,2009
9张学典,王富彦,秦晓飞.基于人工鱼群算法的分数阶PIλ控制器参数整定[J/OL].计算机应用研究,2019,36(3):1-8.http://kns.cnki.net/kcms/detail/51.1196.TP.20180209.1114.022.html,2018-05-07
10刘金琨.先进PID控制MATLAB仿真[M].北京:电子工业出版社,2011