Abstract

Recently, minimum entropy control methods has been successfully used as an information theoretic criterion for non-Gaussian stochastic systems. In this paper, a new single neuron control strategy for nonlinear and non-Gaussian unknown stochastic systems has been proposed in the framework of information theory. Firstly, instead of minimum error entropy criterion, the survival information potential (SIP) criterion, where the randomness of control input is also considered, is formulated. Then, based on the single neuron controller structure, the optimal controller parameters are obtained so that the randomness and magnitude of the closed-loop tracking error is made as small as possible. Finally, this control approach is applied to control the temperature of Organic Rankine Cycle (ORC) processes and encouraging results have been obtained.

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