Hierarchical Multiple Models Neural Network Decoupling Controller for a Nonlinear System
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. Therefore 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. To realize decoupling control, the nonlinear term and the interaction of the system are viewed as measurable disturbance and eliminated using feedforward strategy. The simulation example shows that the better system response can be got comparing with the conventional NN decoupling control method.
KeywordsNonlinear System Equilibrium Point Nonlinear Term Induction Motor Global Asymptotic Stability
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