Implementation of iterative learning control in a walking beam motion system with a flexible coupled robotic mechanism

This paper focuses on the high-precision engineering application of designing and implementing a norm-optimal iterative learning control (ILC) algorithm for the precise positioning of a robotic gripper tip flexibly coupled to an industrial walking beam system. The ILC algorithm generates a feedforward or exogenous signal to minimize tracking errors, thereby enabling high-precision motion control. However, integrating the ILC signal into an existing feedback control system can introduce control signal discontinuities and fluctuations, potentially impairing performance. To address these challenges, this paper proposes structural adjustments to the feedback control algorithm and the implementation of the ILC signal. The primary objectives are to prevent controller fragility caused by the integration of the ILC signal, ensure stable and feasible dynamics of the control variable, and maintain the desired system performance. This work demonstrates the novel application of norm-optimal ILC to a walking beam system with a flexibly coupled robotic mechanism. To the best of our knowledge, this specific application has not been explored in the literature. Validation through simulations, conducted using a nonlinear plant model in Simscape/Simulink, provides valuable insights into the dynamic behavior and control performance of the system.