Nonlinear model-predictive control based on quasi-ARX radial-basis function-neural-network
A nonlinear model-predictive control (NMPC) is demonstrated for nonlinear systems using an improved fuzzy switching law. The proposed moving average filter fuzzy switching law (MAFFSL) is composed of a quasi-ARX radial basis function neural network (RBFNN) prediction model and a fuzzy switching law....
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41489/ |
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| Summary: | A nonlinear model-predictive control (NMPC) is demonstrated for nonlinear systems using an improved fuzzy switching law. The proposed moving average filter fuzzy switching law (MAFFSL) is composed of a quasi-ARX radial basis function neural network (RBFNN) prediction model and a fuzzy switching law. An adaptive controller is designed based
on a NMPC. a MAFFSL is constructed based on the system
switching criterion function which is better than the (ON/OFF) switching law and a RBFNN is used to replace the neural network (NN) in the quasi-ARX black box model which is understood in terms of parameters and is not an absolute black box model, in comparison with NN. The proposed controller performance is verified through numerical simulations to demonstrate the effectiveness of the proposed method. |
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