Anti-noise kinematic controller for redundant manipulators based on model driven neural network

Abstract

In this paper, a model-based anti-noise neural network controller for redundant robot motion control is proposed for motion control of redundant robots with uncertain kinematic parameters. The main challenge of this problem is the coexistence of parameter uncertainty, redundancy resolution, and system physical constraints. Therefore, a new model - driven neural network controller is proposed in this paper. A class of nodes are introduced to deal with the kinematic parameter uncertainty of the system. On this basis, the selection of the initial value of the hyperparameter of the neural network is deeply analyzed, and this processing has a positive effect on accelerating the convergence of the tracking error. The proposed controller has the advantages of simple structure, small computation and simple implementation. The simulation of Kinova Jaco2 manipulator verifies the effectiveness of the proposed algorithm.