Abstract
This work presents the neural network combined with the sliding mode control (NNSMC) to design a robust controller for the two-links robot system. Sliding mode control (SMC) is well known for its robustness and efficiency to deal with a wide range of control problems with nonlinear dynamics. However, for complex nonlinear systems, the uncertainties are large and produce higher amplitude of chattering due to the higher switching gain. In order to reduce this gain, neural network (NN) is used to estimate the uncertain parts of the system plant with on-line training using backpropagation (BP) algorithm. The learning rate is one of the parameters of BP algorithm which have a significant influence on results. Particle swarm optimization (PSO) algorithm with global search capabilities is used in this study to optimize this parameter in order to improve the network performance in term of the speed of convergence. The performance of the proposed approach is investigated in simulations and the control action used did not exhibit any chattering behavior.
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
References
Patino, H.D., Carelli, R., Kuchen, B.R.: Neural networks for advanced control of robot manipulators. IEEE Trans. Neural Networks 13, 343–354 (2002)
Hussain, M.A., Ho, P.Y.: Adaptive sliding mode control with neural network based hybrid models. J. Process Control 14, 157–176 (2004)
Liu, P.X., Zuo, M.J., Meng, M.Q.H.: Using neural network function approximation for optimal design of continuous-state parallel-series systems. Comput. Oper. Res. 30, 339–352 (2003)
Sefreti, S., Boumhidi, J., Naoual, R., Boumhidi, I.: Adaptive neural network sliding mode control for electrically-driven robot manipulators. Control Eng. Appl. Inform. 14, 27–32 (2012)
Slotine, J.J.: Sliding controller design for non-linear systems. Int. J. Control 40, 421–434 (1984)
Utkin, V.I.: Sliding modes in control optimization, Springer (1992)
Slotine, J.J., Sastry, S.S.: Tracking control of nonlinear systems using sliding surfaces with applications to robot manipulators. Int. J. Control 39, 465–492 (1983)
Rumelhart, D.E., Hinton, G.E., Williams, RJ.: Learning internal representations by error propagation. In: Parallel Distributed Processing, vol. 1. Cambridge, MIT Press (1986)
Fu, L.M.: Neural Networks in Computer Intelligence. McGraw-Hill, New York (1995)
Eberhar, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro-Machine and Human Science, pp 39–43 (1995)
Cavuslua, M.A., Karakuzub, C., Karakayac, F.: Neural identification of dynamic systems on FPGA with improved PSO learning. Appl. Soft Comput. 12, 2707–2718 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Massou, S., Boufounas, Em., Boumhidi, I. (2016). Optimized Neural Network Sliding Mode Control for Two Links Robot Using PSO Technique. In: El Oualkadi, A., Choubani, F., El Moussati, A. (eds) Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015. Lecture Notes in Electrical Engineering, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-319-30298-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-30298-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30296-6
Online ISBN: 978-3-319-30298-0
eBook Packages: EngineeringEngineering (R0)