A Nonlinear Hierarchical Multiple Models Neural Network Decoupling Controller

Authors:
Xin Wang;Hui Yang;Shaoyuan Li;Wenxin Liu;Li Liu;David A. Cartes
Affiliations:
Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai, P.R. China 200240 and School of Electrical & Electronic Engineering, East China Jiao Tong University, Nanchan ...;School of Electrical & Electronic Engineering, East China Jiao Tong University, Nanchang, P.R. China 330013;Institute of Automation, Shanghai Jiao Tong University, Shanghai, P.R. China 200240;Center for Advanced Power Systems, Florida State University, Tallahassee, USA 32310;Center for Advanced Power Systems, Florida State University, Tallahassee, USA 32310;Center for Advanced Power Systems, Florida State University, Tallahassee, USA 32310
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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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.