Learning-Enabled Output-Feedback-MPC-Based Synchronization Tracking Control of Multiaxis Motion Systems

The unknown and time-varying model parameters and measurement noises impose great influence on the high-precision synchronization tracking control performance of multiaxis motion systems (MAMSs). To deal with the influence of the factors on the high-precision synchronization tracking control performance in MAMSs, a learning-enabled output-feedback model-predictive control (MPC) strategy for MAMSs is proposed in this article. First, an efficient learning mechanism is introduced to capture the unknown and slow time-varying behavior of the system parameters. Then, considering both the existence of the measurement noises and the unavailability of the velocity signals in system and synchronization coupling errors, the learning-enabled output-feedback-MPC-based synchronization tracking controller is designed to achieve the high-precision synchronization tracking control of the MAMSs. The stability of the overall synchronization tracking system with the proposed learning procedure is analyzed. Finally, simulations and experiments are performed on the four-motor motion system to demonstrate the practicability and effectiveness of the proposed control strategy.