Optimal Control Design Under Limited Model Information for Discrete-Time Linear Systems With Stochastically-Varying Parameters
We design optimal local controllers for interconnected discrete-time linear systems with stochastically varying parameters using exact local model information and statistical beliefs about the model of the rest of the system. We study the value of model information in control design using the closedloop performance degradation caused by the lack of full model information in the control design procedure. This performance degradation is captured using the ratio of the cost of the optimal controller with limited model information over the cost of the optimal controller with full model information. Both finitehorizon and infinite-horizon cost functions are considered. A numerical example illustrates the developed approach.