基于改进细菌觅食算法的摩擦参数辨识方法

Parameter Identification of LuGre Friction Model Based on Improved Bacterial Foraging Optimization

采用LuGre摩擦模型的前馈补偿可抑制非线性摩擦力的影响,提高伺服系统的低速性能。但LuGre摩擦模型高度非线性,动态参数难以准确获取,降低了摩擦补偿的效果。提出一种基于改进细菌觅食算法的LuGre摩擦模型参数辨识方法,引入自适应步长方法以提高动态参数的辨识精度和收敛速度。在MATLAB中实现了该算法,进行了参数辨识,证明该参数辨识方法能获得较高的辨识精度。在MATLAB/Simulink中搭建了伺服电机仿真模型,使用辨识结果进行了摩擦补偿效果验证,仿真结果证明了该方法的有效性。

The feedforward compensation using the LuGre friction model could suppress the influence of nonlinear friction and improved the low-speed performance of the servo system.However,the LuGre friction model was highly nonlinear,and the dynamic parameters were difficult to obtain accurately,which reduced the effect of friction compensation.A LuGre friction model parameter identification method based on an improved bacterial foraging optimization was proposed,and an adaptive step size method was introduced to improve the identification accuracy and convergence speed of dynamic parameters.The proposed optimization was implemented in MATLAB,and parameter identification was carried out,which proved that the proposed parameter identification method could obtain higher identification accuracy.A servo motor simulation model is built in MATLAB/Simulink,and the friction compensation effect was verified by using the identification results.The simulation results prove the effectiveness of the proposed method.