2024/08/22 by Weiyi Yang, Shuai Li, Yang, Weiyi +3
Engineering · #FOS: Electrical engineering #Hydraulic and Pneumatic Systems #Systems and Control (eess.SY) #Vehicle Dynamics and Control Systems #Vibration Control and Rheological Fluids #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2408.12126
openalex publication_date 2024/08/22 · openalex created_date 2024/12/20 · openalex updated_date 2026/07/28
With the rapid development of industry, the vibration control of flexible structures and underactuated systems has been increasingly gaining attention. Input shaping technology enables stable performance for high-speed motion in industrial motion systems. However, existing input shapers generally suffer from the ineffective control performance due to the neglect of observation 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.