Abstract
In a hydraulic support system of heavy equipment, the oil pressure is required to be a constant value. Due to the disturbances come from the external environment and the running process, the hydraulic support system would not be stable; therefore, we here present a closed-loop feedback system, which has an adaptive neuronal network control system to make the oil pressure stable by controlling the rotate speed of the hydraulic pump motor. In this hydraulic system, the response and stability are the key factors to judge the control method is good or not. In our simulation, it shows that this adaptive neural network control system can meet the design requirements. It has good response and stability characteristics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Liu, L.: Uncertain Nonlinear Systems with Adaptive Neural Network Control. Liaoning University (2013)
Liu, H.: Neural Network Based System Identification. Xi’an Electronic Science and Technology University (2007)
Wang, W., Su, S., Xianxian, G.: Research of Adaptive Control Based of Neural Network. Computer Simulation 22(8), 132–135 (2005)
Zhao, Y.: Telescope Hydraulic Control System Based on DSP. Application of Electronic Technique 39(3), 23–26 (2005)
Chen, M.: MATLAB Neural Network Theory and Examples. Tsinghua University (2013)
Piche, S.W.: Steepest Descent Algorithms for Neural Network Controllers and Filters 5(2), 198–212 (1994)
Kan, J.: Self-Tuning PID Controller Based on Improved BP Neural Network. In: Second International Conference on Intelligent Computation Technology and Automation, vol. 1, pp. 95–98. IEEE Press, Changsha (2009)
Tayebi, A.: Reference Adaptive Iterative Learning Control for Linear Systems 20(9), 475–489 (2006)
Cigdem, A.-U., Berna, D.: A self-adapitive local search algorithm for the classical vehicle routing problem. Expert Systems with Applications 38(7), 8990–8998 (2011)
Narendra, K.S., Annaswamy, A.M.: Robust adaptive control in the presence of bounded disturbances. IEEE Transactions on Automatic Control (4), 306–315 (1986)
Hunt, K.J., Sbarbaro, D., Zbikowski, R., Gawthrop, P.J.: Neural networks for control system-asurvey. Automatica (28), 1083–1112 (1992)
Parthasarathy, K.: Identification and control for dynamical systems using neural netwokrs. IEEE Transaction on Neural Networks (1), 4–27 (1990)
Yu, Z.X., Du, H.B.: Adaptive neural tracking control for stochastic nonlinear systems with time-varying delay. Journal of Control Theory and Applications (2), 1808–1812 (2011)
Tong, S.C., Li, Y.M., Zhang, H.G.: Adaptive neural network decentralized back stepping output-feedback control for nonlinear large-scale systems with time delays. IEEE Transactions on Neural Networks (22) 1073–1086 (2011)
Fathi, F.: A greenhouse control with feed-forward and recurrent neural networks. Simulation Modelling Practice and Theory 15(8), 1016–1028 (2007)
Dong, X., Mei, W.: Design of an expert system based on neural network ensembles for missile fault diagnosis. In: Proceedings of 2003 IEEE International Conference on Robotics, vol. (2), pp. 903–908 (2003)
Martin, R., Heinrich, B.: A Direct Adaptive Method for Faster Back-propagation Learning. In: Ruspini, H. (ed.) Proceedings of the IEEE International Conference on Neural Networks, pp. 586–591 (1993)
Patrick, P.: Minimisation Method for Training Feed forward Neural Network. Neural Network (7), 145–163 (1994)
Behera, L., Kumar, S., Patnaik, A.: On adaptive learning rate that guarantees convergence infeed forward networks. Neural Networks 17(5), 1116–1125 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhong, H., Li, D., Tu, K. (2014). A Study of Adaptive Neural Network Control System. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds) Computational Intelligence, Networked Systems and Their Applications. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45261-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-45261-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45260-8
Online ISBN: 978-3-662-45261-5
eBook Packages: Computer ScienceComputer Science (R0)