基于T-S模糊模型的模型参考自适应逆控制

针对非线性系统，提出一种基于T-S模糊模型的模型参考自适应逆扰动消除控制方法。所提方法根据模糊辨识理论与模型参考自适应逆控制各自的特点，将两者相结合。首先，根据模糊系统理论，分别采用模糊对角线划分和递推最小二乘算法进行前提和结论参数辨识，离线辨识得到对象模糊模型和逆对象模糊模型。将辨识出的对象逆设为原始控制器，与被控对象串联；为了分离出系统扰动信号，将辨识出的对象模型与被控对象并联，通过被控系统与对象模型输出做比较，再通过逆对象模型反馈到系统输入端，组成扰动消除环节。用最小均方差算法在系统运行过程中在线调节逆对象模糊模型参数，使其输出误差最小。最后，使用所提方法对一混合非线性系统及输入/输出非线性系统进行仿真试验，仿真结果验证了所提方法的有效性。

Model reference adaptive inverse control based on T-S fuzzy model

Based on T-S fuzzy system, a model reference adaptive disturbance canceling control method is proposed for nonlinear systems. The proposed method combines the fuzzy identification theory with the model reference adaptive inverse control according to their respective characteristics. First, according to the fuzzy identification theory, the premise structure identification is achieved by using diagonal division and the consequent parameters are identified by the recursive least squares method. Then model and inverse model of the plant are obtained in offline manner. The inverse model is connected to the plant in series as the initial controller. The model is parallel to the plant so as to determine the disturbance response. The disturbance is feedback to the input of the plant through the inverse model. While the system is running, the parameters of the controller are adjusted by least mean square adaptive algorithm to minimize the system error. Finally, the proposed method is applied to tracking control of a hybrid nonlinear system and input/output nonlinear system. The simulation results verify the effectiveness of the proposed method.