Abstract
For a nonlinear discrete-time Multi-Input Multi-Output (MIMO) system, a Hierarchical Multiple Models Neural Network Decoupling Controller (HMMNNDC) is designed in this paper. Firstly, the nonlinear system’s working area is partitioned into several sub-regions by use of a Self-Organizing Map (SOM) Neural Network (NN). In each sub-region, around every equilibrium point, the nonlinear system can be expanded into a linear term and a nonlinear term. The linear term is identified by a BP NN trained offline while the nonlinear term by a BP NN trained online. So these two BP NNs compose one system model. At each instant, the best sub-region is selected out by the use of the SOM NN and the corresponding multiple models set is derived. According to the switching index, the best model in the above model set is chosen as the system model. Then the nonlinear term of the system are viewed as measurable disturbance and eliminated by the choice of the weighting polynomial matrices. The simulation example shows that the better system response can be got comparing with the conventional NN decoupling control method.
Keywords
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
Wang, X., Li, S.Y., et al.: Multi-model Direct Adaptive Decoupling Control with Application to the Wind Tunnel. ISA Transactions 44, 131–143 (2005)
Lin, Z.L.: Almost Disturbance Decoupling with Global Asymptotic Stability for Nonlinear Systems with Disturbance-affected Unstable Zero Dynamics. Systems & Control Letters 33, 163–169 (1998)
Lin, Z.L., Bao, X.Y., Chen, B.M.: Further Results on Almost Disturbance Decoupling with Global Asymptotic Stability for Nonlinear Systems. Automatica 35, 709–717 (1999)
Ansari, R.M., Tade, M.O.: Nonlinear Model-based Process Control: Applications in Petroleum Refining. Springer, London (2000)
Khail, H.K.: Nonlinear Systems. Prentice Hall Inc., New Jersey (2002)
Germani, A., Manes, C., Pepe, P.: Linearization and Decoupling of Nonlinear Delay Systems. In: Proceedings of the American Control Conference, pp. 1948–1952 (1998)
Wang, W.J., Wang, C.C.: Composite Adaptive Position Controller for Induction Motor Using Feedback Linearization. IEE Proceedings D Control Theory and Applications 45, 25–32 (1998)
Wai, R.J., Liu, W.K.: Nonlinear Decoupled Control for Linear Induction Motor Servo-Drive Using The Sliding-Mode Technique. IEE Proceedings D Control Theory and Applications 148, 217–231 (2001)
Balchen, J.G., Sandrib, B.: Elementary Nonlinear Decoupling Control of Composition in Binary Distillation Columns. Journal of Process Control 5, 241–247 (1995)
Haykin, S.S.: Neural Networks: A Comprehensive Foundation. Prentice Hall Inc., New Jersey (1999)
Ho, D.W.C., Ma, Z.: Multivariable Internal Model Adaptive Decoupling Controller with Neural Network for Nonlinear Plants. In: Proceedings of the American Control Conference, pp. 532–536 (1998)
Yue, H., Chai, T.Y.: Adaptive Decoupling Control of Multivariable Nonlinear Non-Minimum Phase Systems Using Neural Networks. In: Proceedings of the American Control Conference, pp. 513–514 (1998)
Kohonen, T.: Self-Organizing Feature Maps. Springer, New York (1995)
Hornik, K., Stinchcombe, M., White, H.: Universal Approximation of an Unknown Mapping and Its Derivatives using Multilayer Feedforward Networks. Neural Networks 3, 551–560 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, X., Yang, H., Li, S., Liu, W., Liu, L., Cartes, D.A. (2008). A Nonlinear Hierarchical Multiple Models Neural Network Decoupling Controller. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-87734-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87733-2
Online ISBN: 978-3-540-87734-9
eBook Packages: Computer ScienceComputer Science (R0)