文章编号：1005-9792(2003)03-0285-05
基于遗传算法的参数在线
自调整模糊控制器
谭冠政,胡生员
(中南大学信息科学与工程学院,湖南 长沙,410083)
摘 要：提出了一种基于遗传算法优化的自调整模糊控制器的设计方法.该模糊控制器的运行参数自调整公式以系统响应的最大超调量、调整时间及稳态误差为性能指标,利用遗传算法搜索模糊控制器量化因子Ke,Kec及比例因子Ku公式中相应的最优基准值Keo,Keco及Kuo和微调参数K1,K2及K3,设计一个根据系统当前的动态误差e运行参数可在线自调整的模糊控制器,以确保系统的响应具有最优的动态和稳态性能.该控制器已用于控制由作者设计的智能人工腿中的执行电机.计算机仿真结果表明,与运行参数固定的模糊控制器相比,这种自调整模糊控制器具有良好的动态和稳态性能.
关键字：遗传算法;在线;自调整;模糊控制器;智能人工腿
Parameter on-line self-tuning fuzzy controller based on genetic algorithm
TAN Guan-zheng,HU sheng-yuan
（College of Information Science and Engineering, Central South University, Changsha 410083, China）
Abstract:A self-tuning fuzzy controller is proposed based on the genetic algorithm optimization. By taking the overshoot, regulation time, and steady-state error of system response as the performance indexes and by means of the genetic algorithm, a group of optimal benchmarks Keo,Keco, Kuo and a group of tiny adjusting parameters K1,K2,K3 in the formulae of quantization factors Ke,Kecand scale factorKuare obtained, which are designed as the operational parameters of the self-tuning fuzzy controller. These operational parameters can be automatically adjusted according to the system′s current erroreto ensure that the system response has optimal dynamic and steady-state performances. The controller has been used to control the motor of the intelligent artificial leg designed by the authors. The result of computer simulation shows that it has more excellent control performance than the fuzzy controller whose parameters are fixed in the process of system response.
Key words:genetic algorithm; on-line; self-tuning; fuzzy controller; intelligent artificial leg
