基于CMAC神经网络的超磁致伸缩非线性控制

对于超磁致伸缩材料固有的迟滞非线性特性，本文提出一种基于小脑模型神经网络(CMAC)前馈逆补偿与PID相结合的复合控制方法。首先利用CMAC神经网络学习获得超磁致伸缩致动器(GMA)的迟滞逆模型进行补偿，再利用CMAC模型在线快速学习适应的能力，结合PID控制器降低跟踪控制时的误差和扰动，从而实现GMA的精密控制。通过MATLAB建立了CMAC前馈逆补偿控制器和CMAC-PID复合控制模型，最后通过仿真实验验证所提方法的有效性。结果表明，提出的利用CMAC神经网络逼近的迟滞模型具有令人满意的精度，在CMAC-PID复合控制方案的作用下，系统的期望位移与实际位移相对误差值最大值仅2.39%,平均相对误差值不到0.5%。说明该控制策略能适应控制对象的非线性变化，有效地提高GMA的跟踪精度。

Nonlinear control of giant magnetostriction based on CMAC neural network

For the inherent hysteresis nonlinearity of magnetostrictive materials, this paper presents a composite control method based on CMAC (cerebellar model neural network) feedforward inverse compensation and PID. Firstly, CMAC neural network is used to learn and obtain the hysteresis inverse model of giant magnetostrictive actuator (GMA) for compensation, and then the CMAC model is used to learn and adapt online quickly, and PID controller is used to reduce the error and disturbance during tracking control, so as to realize the precision control of GMA. CMAC feedforward inverse compensation controller and CMAC-PID compound control model are established by MATLAB. Finally, the effectiveness of the proposed method is verified by simulation experiments. The results show that the proposed hysteresis model approximated by CMAC neural network has satisfactory accuracy. Under the action of CMAC-PID composite control scheme, the maximum relative error between the expected displacement and the actual displacement of the system is only 2.39%, and the average relative error is less than 0.5%. It shows that the control strategy can adapt to the nonlinear change of the control object and effectively improve the tracking accuracy of GMA.