A fuzzy CMAC learning approach to image based visual servoing system

Abstract

This study presents a fuzzy robotic joint controller using a cerebellar model articulation controller (CMAC) integrating a Takagi-Sugeno (T-S) framework with an online compensator for an articulated manipulator. The proposed controller is applied to image-based visual servoing (IBVS), including closed-loop feedback control and the kinematic Jacobian calculation. This approach learns a mapping from image feature errors for each joint’s velocity instead of the classical kinematics, thereby reducing the computational complexity and improving the self-regulation ability of the control system. These connecting weights of the cerebellar model learn offline, and an online compensator that uses reinforcement learning is developed to resolve system noise and uncertainties in an unknown environment. Compared with the classical inverse kinematics model, this approach does not need an excessive computational expense so that this proportional controller can be implemented in general scenarios with an eye-in-hand configuration. Experimental results show the proposed method can outperform the classical IBVS controller.