Neural network based bicriterial dual control of nonlinear systems
| Authors: | Simandl Miroslav, University of West Bohemia in Pilsen, Czech Republic
Kral Ladislav, University of West Bohemia in Pilsen, Czech Republic
Hering Pavel, University of West Bohemia in Pilsen, Czech Republic |
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| Topic: | 3.2 Cognition and Control ( AI, Fuzzy, Neuro, Evolut.Comp.) |
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| Session: | Neural Control |
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| Keywords: | Neural networks, adaptive control, stochastic control, nonlinear systems, parameter estimation, non-Gaussian processes, learning algorithms |
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Abstract
A bicriterial dual controller for nonlinear stochastic systems is suggested. Two separate criterions are designed and used tointroduce one of opposing aspects between estimation and control; caution and probing. A system is modelled using a multilayer perceptron network. Parameters of the network are estimated by the Gaussian sum method which allows to determine conditional probability density functions of the network weights. The proposed approach is compared with inovation dual control and the quality of the estimator and the regulator is analyzed by simulation and Monte Carlo analysis.
