Fuzzy Neural Dynamics for Kinematic Learning and Simultaneous Control of Redundant Robots

The accuracy of the kinematic information of a redundant robot directly influences the control performance of its end-effector. However, in practical applications, kinematic parameters may change. To address this challenge, this paper proposes a robust learning and fuzzy control (RLFC) model that possesses kinematic learning and simultaneous control capabilities, enabling the estimation of unknown kinematic parameters and achieving adaptive control for redundant robots. Specifically, the RLFC model comprises two components: an integral-enhanced recurrent neural dynamics (IERND) and a fuzzy-enhanced recurrent neural dynamics (FERND). The IERND is designed to enhance the noise tolerance in kinematic parameter learning. Based on the learned kinematic parameters, the FERND, which incorporates a fuzzy logic system for dynamically adjusting convergence parameters, achieves effective position and orientation tracking of the end-effector in the redundant robot. Theoretical analyses are provided to demonstrate the convergence of the RLFC model. Simulations and experiments validate the effectiveness of the RLFC model in parameter learning, noise tolerance, and kinematic control. The accompanying video can be accessed athttps://youtu.be/DS6k7Cu19sU.