Robust Control of Robotic Manipulators Based on Adaptive Neural Network

Wenhui Zhang*, Xiaoping Ye, Lihong Jiang, Fang Yamin
College of Technology, Lishui University, City, 323000, P.R. China

© 2014 Wenhui et al

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: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the College of Technology, Lishui University, City, Xueyuan Road No 1, Lishui, Zhejiang Province, 323000, P.R. China; Tel: 05782271308; Fax: 05782271308; E-mail:


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.

Keywords: Adaptive control, Neural network, Robotic manipulators, Robust control.