Abstract
A new robust adaptive neural networks tracking control with online learning controller is proposed for robot systems. A learning strategy and robust adaptive neural networks are combined into a hybrid robust control scheme. The proposed controller deals mainly with external disturbances and nonlinear uncertainty in motion control. A neural network (NN) is used to approximate the uncertainties in a robotic system. Then the disadvantageous effects on tracking performance, due to the approximating error of the NN in robotic system, are attenuated to a prescribed level by an adaptive robust controller. The learning techniques of NN will improve robustness with respect to uncertainty of system, as a result, improving the dynamic performance of robot system. A simulation example demonstrates the effectiveness of the proposed control strategy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hwang, M.C., Hu, X.: A Robust Position/Force Linear Controller of Manipulators via Nonlinear H∞ Control and Neural Networks. IEEE Transactions on System, Man, Cybernetics–Part B 30(2), 310–321 (2000)
Dixon, W.E., Zergeroglu, E., Dawson, D.M.: Global Robust Output Feedback Tracking Control of Robot Manipulators. Robotica 22(4), 351–357 (2004)
Wang, D., Soh, Y.C., Chean, C.C.: Robust Motion and Force Control of Constrained Manipulators by Learning. Automatica 31(2), 257–262 (1995)
Kung, S.Y., Hwang, J.N.: Neural Network Architectures for Robotic Applications. IEEE Transactions on Robotics and Automation 5(5), 641–657 (1989)
Bai, P., Fang, T.J., Ge, Y.J.: Robust Neural-Network Compensating Control for Robot Manipulator Based on Computed Torque Control. Control Theory and Applications 18(6), 895–903 (2001)
Nasisi, O., Carelli, R.: Adaptive Servo Visual Robot Control. Robotics and Autonomous Systems 43(1), 51–78 (2003)
Wai, R.J.: Robust Control for Nonlinear Motor-Mechanism Coupling System Using Wavelet Neural Network. IEEE Transactions on Systems Man and Cybernetics-Part B: Cybernetics 33(3), 489–497 (2003)
Park, J., Sandberg, I.W.: Universal Approximation Using Radial-Basis-Function Networks. Neural Computation 3(2), 246–257 (1991)
Liao, Y., Fang, S.C., Nuttle, H.L.W.: Relaxed Conditions for Radial-Basis Function Networks to Be Universal Approximators. Neural Networks 16(7), 1019–1028 (2003)
Leung, T.P., Zhou, Q.J., Su, C.Y.: An Adaptive Variable Structure Model Following Control Design for Robot Manipulators. IEEE Trans. Automatic Control 36(3), 347–353 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, Zg., Song, Sm., Duan, Gr., Pei, R. (2006). Robust Adaptive Neural Networks with an Online Learning Technique for Robot Control. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_169
Download citation
DOI: https://doi.org/10.1007/11760023_169
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)