摘要
提出一种基于优化算法的非线性系统参数辨识的方法。针对直流电机非线性摩擦力和饱和特性,建立直流电机的非线性模型。利用遗传算法的全局寻优特性和单纯形法快速的收敛性,通过获取充分激励的实际系统运行的输入输出数据,辨识出包括非线性摩擦力在内的直流电机所有的8个模型参数。获取系统实际运行的输入输出数据,分别验证电机运行于死区、饱和区和线性区时,模型辨识的精度。这种方法可以方便地应用于其他非线性系统参数辨识中。
A nonlinear system identification method based on optimization algorithms is proposed. The nonlinear DC motor model is established based on the characteristics of nonlinear friction and saturation of DC motor. Through obtaining sufficient excitation input-output data of the actual system, all eight parameters of DC motor model including the nonlinear friction model are identified by using the global optimization character of the genetic algorithm and the fast convergence of the simplex method. By using the actual input-output data, precision of these identified parameters are verified when the motor running in the dead zone, saturated zone and linear zone, respectively. The proposed method can be used to identify other nonlinear systems.
出处
《控制工程》
CSCD
北大核心
2009年第1期109-112,共4页
Control Engineering of China
基金
国家自然科学基金资助项目(60774098)
关键词
直流电机
参数辨识
遗传算法
单纯形法
非线性摩擦力
DC motor
parameter identification
genetic algorithm（GA）
simplex method
nonlinear friction
