Cerebellum-Inspired Learning and Control Scheme for Redundant Manipulators at Joint Velocity Level.

Redundant manipulators, as mechanical equipments imitating human arms, have been applied to various areas in recent years from the perspective of control. Different from pure control technologies, the motion capability of a human arm is achieved by a complex and efficient neural system, with the cerebellum playing a pivotal role. Motivated by this fact, we design a cerebellum model based on an echo state network (ESN) for the learning and control of redundant manipulators. In addition, to simulate the skillful control ability of the cerebellum over movements of human arms, the proposed model is constructed at the joint velocity level. Furthermore, to improve the accuracy and applicability, we propose an ESN-based Kalman-filter-incorporated and cerebellum-inspired (KFICI) scheme for the learning and control of redundant manipulators with Kalman filter incorporated. The proposed scheme enables a redundant manipulator to track the desired trajectory at the velocity level and tolerate noises. Finally, simulations and experiments based on a physical redundant manipulator are performed to verify the effectiveness of the proposed control scheme.