基于人类学习优化算法的变论域模糊PID控制研究

Variable Universe Fuzzy-PID Control Based on Human Learning Optimization Algorithm

针对一些复杂的工业控制系统中利用常规的PID控制往往难以达到理想的控制性能,以及控制系统中控制器的相关参数难以精确设定的问题,提出了一种基于人类学习优化算法(Human Learning Optimization,HLO)的变论域模糊PID控制方法并应用于电站的优化控制当中。首先介绍了HLO算法以及变论域模糊PID控制方法的相关原理,然后阐述了运用HLO算法对变论域模糊PID控制器进行优化设计的实现过程,接着和基本的模糊PID控制器以及常规PID控制器的控制性能进行了对比,并与基于粒子群算法、差分进化算法和声搜索算法的变论域模糊PID控制器的控制性能进行了对比。结果表明,基于HLO算法的变论域模糊PID控制器具有更好的控制效果,且实现简便,具有良好的应用前景。

This paper proposes a variable universe Fuzzy-PID control method based on human learning optimization（HLO） algorithm and apply it to power plant controlling.Firstly,the principle of human learning optimization algorithm and variable universe fuzzy PID control method are introduced,and then the design process of variable universe fuzzy PID controller based on HLO is introduced.At last,the performance of variable universe fuzzy PID controller based on HLO are compared with the basic fuzzy PID controller and conventional PID controller,besides,it also compared with variable universe of fuzzy PID controller based on PSO、DE and HS.