蚁群优化算法在FPID参数调节中的应用研究  

Ant Colony Optimization Algorithm and Its Application to Fuzzy Controller Parameter Adjustment

作　　者：徐开军[1] 张春艳[1] 张磊[1,2] 张汉年[1] 

机构地区：[1]南京信息职业技术学院,南京210046 [2]南京航空航天大学自动化学院,南京210016

出　　处：《计算机测量与控制》2013年第5期1278-1280,1284,共4页Computer Measurement &Control

基　　金：南京信息职业技术学院科研基金项目(YKJ11-008)

摘　　要：蚁群优化算法(ACOA)是一种群体智能(SI)技术,在各种组合优化问题上的成功应用证明了它是一种有效的仿生优化方法;模糊PID控制器已广泛地应用于工业生产的许多领域,但是其参数调节问题一直受到知识经验缺乏的困扰,针对该问题,通过对蚁群算法的分析改进,提出了一种利用蚁群优化算法来调节典型的模糊PID控制器的方法;该方法将模糊PID控制器的参数赋予一定的物理意义,使其调节比传统的的PID控制器更容易;引入一个倒立摆垂直部分的稳定问题来验证该方法,实验结果表明,得到一个可接受的解决方案不超过20次迭代,缩短了优化时间,提高了调节效率。Ant colony optimization algorithm （ACOA） is one of the swarm intelligence （SI） techniques. It is a bio--inspired optimization method that has proven its success through various combinatorial optimization problems. Fuzzy PID controllers have been widely used in many areas of industrial production, but its parameter adjustment has been troubled by the lack of knowledge and experience. In order to solve the problem, proposed an ant colony optimization algorithm for tuning the typical fuzzy PID controller through analysis and improving ant colony algorithm. The tuning parameters of these controllers have physical meaning which makes its tuning task easier than conventional PID controllers. Introducing a stability of the inverted pendulum to validate the method, the experimental results show that, to obtain an acceptable solution is not more than 20 iterations, shorten optimization time and improve adjustment efficiency.

关 键 词：蚁群优化算法(ACOA) PID控制器 模糊规则 隶属度函数 倒立摆 

分 类 号：TP273.4[自动化与计算机技术—检测技术与自动化装置]