改进粒子群算法在空调水系统中的应用研究
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摘要
粒子群算法优化PID控制在空调系统中应用越来越广泛.针对传统粒子群算法在优化过程中存在寻优速度不足和容易陷入局部最优的问题,提出了采用正交试验机制和模仿染色体变异机制的突变策略优化传统粒子群算法,通过引入正交试验机制提升了寻优速度和效率,引入突变策略改进了算法的极值传递过程,避免了过早发生聚集现象,解决了陷入局部最优的问题.仿真实验结果证明:改进粒子群算法在系统超调量、调节时间、稳态误差和抗干扰性上均有显著提高.
The particle swarm optimization PID control is applied more and more widely in the air conditioning system. In order to solve the problem that the traditional particle swarm optimization algorithm has insufficient optimization speed and easy to fall into the local optimum in the process of optimization, the traditional particle swarm optimization algorithm is optimized by using the orthogonal experiment mechanism and the mutation strategy that mimics the chromosome mutation mechanism. The optimization speed and efficiency are improved by introducing the orthogonal experiment mechanism, and the mutation is introduced by introducing the mutation. The algorithm improves the extreme value transfer process of the algorithm, avoids premature clustering and solves the problem of falling into local optimum. The simulation results show that the improved particle swarm optimization algorithm significantly improves the overshoot, adjustment time, steady state error and anti-interference capability of the system.
The particle swarm optimization PID control is applied more and more widely in the air conditioning system. In order to solve the problem that the traditional particle swarm optimization algorithm has insufficient optimization speed and easy to fall into the local optimum in the process of optimization, the traditional particle swarm optimization algorithm is optimized by using the orthogonal experiment mechanism and the mutation strategy that mimics the chromosome mutation mechanism. The optimization speed and efficiency are improved by introducing the orthogonal experiment mechanism, and the mutation is introduced by introducing the mutation. The algorithm improves the extreme value transfer process of the algorithm, avoids premature clustering and solves the problem of falling into local optimum. The simulation results show that the improved particle swarm optimization algorithm significantly improves the overshoot, adjustment time, steady state error and anti-interference capability of the system.
引文
[1] 王姝,陈崚.基于正交试验设计的粒子群优化算法[J].扬州大学学报(自然科学版),2010,13(2):57-60,64.
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[4] 郝万君,强文义,胡林献,等.基于改进粒子群算法的PID参数优化与仿真[J].控制工程,2006(5):429-432.
[5] 陈之启.空调系统PID控制的二次型组合优化[J].智能建筑与城市信息,2011(1):61-67.
[6] 于雪晶,麻肖妃,夏斌.动态粒子群优化算法[J].计算机工程,2010,36(4):193-194,197.
[7] 任小波,杨忠秀.粒子群优化算法的改进[J].计算机工程,2010,36(7):205-207.
[2] 徐志成,王琳.基于改进微粒群算法的PID控制器参数优化设计[J].电工电气,2009(1):21-24.
[3] 刘衍民,赵庆祯,牛奔.基于正交设计的多目标粒子群算法[J].计算机应用研究,2011,28(1):72-74.
[4] 郝万君,强文义,胡林献,等.基于改进粒子群算法的PID参数优化与仿真[J].控制工程,2006(5):429-432.
[5] 陈之启.空调系统PID控制的二次型组合优化[J].智能建筑与城市信息,2011(1):61-67.
[6] 于雪晶,麻肖妃,夏斌.动态粒子群优化算法[J].计算机工程,2010,36(4):193-194,197.
[7] 任小波,杨忠秀.粒子群优化算法的改进[J].计算机工程,2010,36(7):205-207.