Deep GRU Neural-Network Prediction and Feedforward Compensation for Precision Multi-Axis Motion Control Systems
Chuxiong Hu, Tiansheng Ou, Haonan Chang, Yu Zhu, Limin Zhu
Abstract
This article proposes a gated recurrent unit (GRU) neural network prediction and compensation (NNC) strategy for precision multiaxis motion control systems with contouring performance orientation. First, some characteristic contouring tasks are carried out on a multiaxis linear-motor-driven motion system, and the true contouring error values obtained by the Newton numerical calculation method are used as the training data of a developed artificial GRU neural network. Essentially, the proposed GRU neural network structure can be viewed as a data-based black-box error model, which can capture the dynamic characteristics of contouring motion rather accurately. The well-trained GRU network can predict the contouring error precisely even under the tasks those have not been conducted during the training session. Moreover, the predicted contouring error is compensated into the reference contour as feedforward compensation to improve the final contouring performance. Comparison between the predicted contouring error and the actual contouring error practically proves the effective prediction ability of the proposed GRU neural network. Furthermore, comparative experiments among proportional-integral-differential, iterative learning control (ILC), and the proposed NNC controller are conducted. The results consistently validate that NNC can basically achieve excellent contouring motion performance as ILC, significantly without need of motion repetition and iteration. Due to the implementation convenience and excellent prediction/compensation ability, the proposed NNC would have good potential in industrial mechatronic applications.