Control Of Nuclear Research Reactors Based On A Generalized Hopfield Neural Network
نویسندگان
چکیده
ABSTR4CT-The purpose of tliis paper is to prcsellt a solution to the n~ininlization prohlenl of the transient time to accomplish the switching hetween different levels of power in a nuclear research reactor satislying the inverse period cons~rairtt and avoiding to use any physical model of the plant. 'l'be strategy liere proposed consists of two stazes. first. the optinul trajectory which satisfies the constraint is calculated 00-line: second. a control law based on a generalized Hopfield neuml network is employed to assure that the reactor power l'ollows this optimal tra,iectory. The boundedness Ibr both the weights and the identification error is guaranteed by a new online learning law. Likewise, proposed control law guarantees an n p p r bound for the tracking error. The elfbclive~iess of this procedure is illustrated by numeric sinlularion.
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عنوان ژورنال:
- Intelligent Automation & Soft Computing
دوره 16 شماره
صفحات -
تاریخ انتشار 2010