Self-Organizing Approach to the Stochastic Fuel Regulator Problem

Abstract

A self-organizing procedure to achieve a performance adaptive controller with asymptotically optimal properties is proposed for systems with completely or partially unknown dynamics. A physically realizable controller that operates in an unknown stochastic environment is obtained. The accrued cost during a global random search for the minimum converges to the minimum value corresponding to the specific optimal controller. The global search strategy includes a subgoal defined on a nondecreasing time subinterval and an algorithm of adaptive random type. The stochastic fuel regulator problem with random switching delay is used as an application of the method and simulation results demonstrate its effectiveness.