Precision Tracking Motion Control Using Deep GRU Network

To meet the requirement of high-performance motion accuracy and complex trajectory variation in modern industry, a neural network feedforward control framework is proposed in this article. This framework is based on the latest recurrent network structure (RNN) method with gate recurrent unit (GRU). By predicting the tracking error of a normal PID controller using deep GRU network, the proposed framework can achieve equal control performance with iterative learning control (ILC) without any actual iterations. Furthermore, the proposed framework can effectively tackle the problem of trajectory variation which is the widely recognized obstacle for ILC. Comparative experiments between traditional PID, ILC, and the proposed GRU control scheme (GRUC) are carried out on a linear-motor-driven stage. Essentially, the proposed GRUC scheme provides a rather excellent feedforward control scheme without iteration and trajectory repetition, and has good potential in industrial applications.