Abstract
The unknown and time-varying model parameters and measurement noises impose great influence on the highprecision 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. Firstly, 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 overall synchronization tracking system with proposed learning procedure is analyzed. Finally, simulations and experiments are performed on fourmotor motion system to demonstrate the practicability and effectiveness of the proposed control strategy.
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