Closed-LSTM neural network based reference modification for trajectory tracking of piezoelectric actuator

In this article, we propose a trajectory tracking control method for piezoelectric actuators (PEAs) based on long short-term memory neural network (LSTM-NN). Different from traditional framework where is used to approximate the open-loop PEA dynamics, LSTM-NN establish mapping between actual and reference of closed-loop PEA, leading Closed-LSTM framework. With framework, trained modify compensate error without changing controller. First, analyze simplify modeling linear nonlinear characteristics select training input features LSTM-NN. Then, use train The enables independent designs baseline feedback controller feedforward compensator. particular, guarantee system stability, modification module as compensator achieve high-precision tracking, which does not affect stability can be easily applied off-the-shelf motion systems. Its validity experimentally verified platform.