Functional adaptive controller for multivariable stochastic systems with dynamic structure of neural network |
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Authors: | L Král M ?imandl |
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Affiliation: | Department of Cybernetics, University of West Bohemia, Univerzitni 8, 306 14 Pilsen, Czech Republic |
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Abstract: | The article deals with a challenging problem of adaptive control design for multivariable stochastic systems with a functional uncertainty. Model of the system is based on multi‐layered perceptron neural networks where both the unknown parameters and the structure are found in real time without a necessity of any off‐line training process. The unknown parameters are estimated by a global estimation method, the Gaussian sum filter, and the structure of the neural network model is optimized by a proposed pruning method. The control law is based on a bicriterial approach to the suboptimal dual control. Two individual criteria are designed and used to introduce conflicting efforts between the estimation and control; probing and caution. A comparison of the proposed dual control and its alternative with an implementation of the pruning algorithm is shown in a numerical example. Copyright © 2011 John Wiley & Sons, Ltd. |
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Keywords: | adaptive control neural networks nonlinear estimation stochastic control |
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