Cerebellum-Inspired Model Predictive Control for Redundant Manipulators With Unknown Structure Information

When the structure information of a redundant manipulator is unknown, motion control methods that do not rely on its model are attractive. Due to the numerous advantages of model predictive control (MPC), such as the direct handling of constraints, this article proposes a model-free MPC algorithm for redundant manipulators with unknown structure information. In this article, a cerebellum-inspired model based on the echo state network (ESN) is employed to replace the kinematic model of the redundant manipulator, and an MPC algorithm based on the cerebellum model and neural dynamics (ND) approach is developed. Unlike existing studies, this work considers both performance optimization and system constraints of the redundant manipulator, and can achieve high-precision prediction and tracking by designing an online training algorithm for the cerebellum model. Furthermore, this article proposes an ND-based correction algorithm to modify the prediction model and an ND solver to solve the MPC scheme. Theoretical analyses confirm the convergence of both the ND-based correction algorithm and ND solver. Simulation and experimental results consistently demonstrate that the proposed cerebellum-inspired MPC (CIMPC) algorithm is effective and outperforms comparison algorithms in terms of tracking performance.