Robust Control of Robotic Manipulators Based on Adaptive Neural Network
Wenhui Zhang*, Xiaoping Ye, Lihong Jiang, Fang Yamin
Identifiers and Pagination:Year: 2014
First Page: 497
Last Page: 502
Publisher Id: TOMEJ-8-497
Article History:Received Date: 10/09/2014
Revision Received Date: 05/11/2014
Acceptance Date: 05/11/2014
Electronic publication date: 24/12/2014
Collection year: 2014
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
As robotic manipulators are increasingly applied in industrial production, higher precision control methods are being studied by researchers. But robotic manipulators are a coupled system with a lot of uncertainties; higher precision is difficult to obtain by traditional control methods. A novel adaptive robust control method based on neural network is proposed by the paper. Neural network controller has been designed for adaptive learning and compensate for the unknown system and approach errors as disturbance is eliminated by robust controller. The weight adaptive laws on-line based on Lyapunov theory are designed. Robust controller is proposed based on H theory. These can assure the stability of the whole system, and L gain also is less than the index value. Simulation studies show that the proposed control strategy is able to achieve higher control precision and has important engineering applications value.