A Data-Driven Cyclic-Motion Generation Scheme for Kinematic Control of Redundant Manipulators

Abstract

Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation (CMG) task, to some extent. Inspired by this problem, this article proposes a data-driven CMG scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously to complete the kinematic control of manipulators with model unknown. It is worth mentioning that the proposed method is capable of accurately estimating the Jacobian matrix in order to obtain the structure information of the manipulator and theoretically eliminates the tracking errors. Theoretical analyses prove the convergence of the learning and control parts under the necessary noise conditions. Computer simulation results and comparisons of different controllers illustrate the reliability and superior performance of the proposed method with strong learning ability and control ability. This article is greatly significant for redundancy resolution of redundant manipulators with unknown models or unknown loads in practice.