Intelligent Compensation Control Using Fuzzy Inference and Recurrent Neural Network for Magnetic Levitation System

To address the control uncertainty and improve the control performance of the magnetic levitation system, an intelligent feedforward compensation controller is proposed based on fuzzy inference (FI) and recurrent neural network (RNN). It consists of a PID-based baseline controller, an RNN-based inverse model identifier, an RNN-based feedforward compensator, and an FI-based automatic regulator. The PID-based baseline controller is applied to provide the initial control law. The RNN-based inverse model identifier is built to online learn the controlled object, while the RNN-based feedforward compensator is built to generate a real-time compensation control law based on the learned parameters. Moreover, the FI-based automatic regulator is designed to dynamically adjust the compensation quantity and adaptively restrain the control uncertainty according to the control error and its change. The effectiveness and advancement of the proposed intelligent controller are experimentally verified by the position-tracking control of the magnetic levitation system. Tracking results of step and square signals indicate that the proposed intelligent controller not only enhances the transient quality but also improves the overall control performance compared to other comparative controllers.