提出了基于内积的压电陶瓷动态神经网络非线性、非光滑迟滞逆模型,采用反馈误差学习方法,快速地在线得到压电陶瓷的逆模型,避免了通过正模型求取压电陶瓷的Jacobian信息。结合PID反馈控制,在dSPACE系统平台上实现了压电陶瓷的神经网络自适应逆控制。为提高实时性,采用了效率高、速度快的C-MEX S Function编程。实验结果表明:神经网络自适应逆控制的控制精度为0.13μm,而PID控制精度为0.32μm。所提出方法有效地消除了迟滞的影响,控制精度高。

In order to improve the actuator precision,a method for eliminating nonlinear and no-smooth hysteresis characteristic of piezoceramic actuator was proposed.An inner product-based dynamic neural network nonlinear and no-smooth hysteresis inverse model for piezoceramic was established,in which the feedback error learning method was used to avoid obtaining Jacobian information of piezoceramic by positive model.On dSPACE system platform,a neural networks adaptive inverse control was realized combined with a PID control.In order to satisfy the requirement of real time control,the program was designed by a high efficiency and fast C-MEX S function.The experimental results indicate that the precision of the proposed adaptive inverse control based on neural networks is 0.13 μm and PID control precision is 0.32 μm.It is shown that the proposed control method can remove effectively the hysteresis characteristic of piezoceramic and has higher control precision.