Vibration Control using A Robust Input Shaper via Extended Kalman Filter-Incorporated Residual Neural Network

With the rapid development of industry, there is a growing concern over the vibration control challenges associated with flexible structures and underactuated systems. Input shaping technology enables stable performance for high-speed motion in industrial motion systems. However, existing input shapers commonly suffer from the ineffective control performance caused by the ignorance of observation error errors. To address this critical issue, this paper proposes an Extended Kalman Filter-incorporated Residual Neural Network-based input Shaping (ERS) model for vibration control. Its main ideas are two-fold: a) adopting an extended Kalman filter to address a vertical flexible beam's model errors; and b) adopting a residual neural network to cascade with the extended Kalman filter for eliminating the remaining observation errors. Detailed experiments on a real dataset collected from a vertical flexible beam demonstrate that the proposed ERS model has achieved significant vibration control performance over several state-of-the-art models.