- Uncertainties in the physical model of large scale systems often induce instability and unpredicted costs. Thus one of the major challenges in the control of large scale systems besides stability is the cost paid by the system. This cost is often defined in a supervisory quadratic form for the whole large scale system. To simplify the design procedures and reduce the computational burden, the large scale system should be decomposed into several lower order subsystems. Though, with such uncertainties and distributed computations a method which gives a proper upper bound for the cost function of the system is necessary. This approach is called guaranteed cost control which has the advantage of providing an upper bound on a given performance index. Thus the system performance degradation incurred by the uncertainties is guaranteed to be less than this bound. On the other hand, the time delay of information transmission induced by communication networks between subsystems is a source of instability and poor performance of the system. Hence, the stabilization and performance analysis of uncertain large scale systems with time delays has received considerable attention by many researchers. In this thesis, we consider a stroked="f" filled="f" path="m@4@5l@4@11@9@11@9@5xe" o:preferrelative="t" o:spt="75" coordsize="21600,21600" controller is designed for uncertain and certain system based on Markovian theory combined with LMI techniques such that the closed loop cost function value is not more than a speci?ed upper bound. Note that the controller obtained varies according to the delay, packet loss, and packet disordering such that the better performance is obtained by the systems. In addition, numerical examples are given to illustrate the effectiveness of the proposed method. Keywords: Supervisory cost function, Guaranteed Cost Control, Large Scale Systems, Linear Matrix Inequality, Lyapunov-Krasovski functional, Gain Scheduling Controller.
