Online learning fuzzy echo state network with applications on redundant manipulators - Li, Yanqiu; Liu, Huan; Gao, Hailong (Frontiers in Neurorobotics, 2024)
Redundant manipulators are universally employed to save manpower and improve work efficiency in numerous areas. Nevertheless, the redundancy makes the inverse kinematics of manipulators hard to address, thus increasing the difficulty in instructing manipulators to perform a given task. To deal with this problem, an online learning fuzzy echo state network (OLFESN) is proposed in the first place, which is based upon an online learning echo state network and the Takagi-Sugeno-Kang fuzzy inference system (FIS). Then, an OLFESN-based control scheme is devised to implement the efficient control of redundant manipulators. Furthermore, simulations and experiments on redundant manipulators, covering a UR5 and a Franka Emika Panda manipulator, are carried out and verify the effectiveness of the proposed control scheme.