Iterative learning dual axis predictive control for flexible robotic arm

Based on iterative learning and model predictive control, for flexible robotic arms in industrial processes with a large number of repetitive operations, and common single iteration learning control or model predictive control algorithms have no effective control effect on the accumulated interference during the iteration process, as the number of iterations increases, the resistance effect of such controllers to accumulated interference becomes worse and worse, and even uncontrollable situations may occur. This article introduces predictive control on the iteration axis to optimize the iterative learning predictive control algorithm. This optimized dual axis predictive control algorithm can achieve high-precision control of robotic arms and effectively suppress errors accumulated during the iteration process. In addition, the effectiveness of the iterative learning dual axis predictive control algorithm was verified through a trajectory tracking simulation experiment of a two degree of freedom flexible robotic arm.