Potentials based optimization with embedded Markov chain for stochastic constrained system |
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Authors: | Kang Cheng Kan-Jian Zhang |
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Institution: | 1.Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education,Southeast University,Nanjing,China;2.School of Automation,Southeast University,Nanjing,China |
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Abstract: | In this paper, a RBF neural network based on-line optimization algorithm with performance potentials analysis method is presented
for a class of stochastic constrained dynamic systems. The control signals of the considered systems are constrained to a
range according to a subset of the whole state space. With the conception of an embedded Markov chain, an optimization approach
on the basis of potentials is presented for a stochastic constrained system, where the optimization criterion is the long-time
average performance. With this approach, the computation burden has been reduced because it only requires one to compute the
control strategy on the states concerned, which are a subset of the whole state space. Furthermore, with the characteristic
of approximation performance of RBF neural network, the potentials and the transition probability matrix are estimated conveniently
by a sample path compared with the statistic approach or the method by solving the Poisson equation. The effectiveness of
the optimization approach has been shown by the simulation results, finally. |
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Keywords: | |
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