The control of stochastic systems presents additional an- alytical challenges but offers considerable advantages since modeling uncertainty as stochastic processes makes control designs less conservative than assuming worst-case bounds [29].

I. INTRODUCTION Systems of coupled Riccati equations occur in several classes of optimal control problems such as jump linear control systems [1]. In this class of control problems, a set of strongly coupled Riccati equations is to be solved to determine the optimal feedback gains. A homotopy algorithm for solving systems of coupled differential Riccati equations of jump linear systems is ...

We consider the problem of controlling an unknown stochastic linear system with quadratic costs – called the adaptive LQ control problem. We re-examine an ap-proach called “Reward-Biased Maximum Likelihood Estimate” (RBMLE) [1] that was proposed more than forty years ago, and which predates the “Upper Confi-dence Bound” (UCB) method ...

He received the Donald P. Eckman Award of the American Automatic Control Council in 1985, and is the recipient of the 2006 IEEE Field Award in Control Systems. He is a coauthor of the book, Stochastic Systems: Estimation, Identification and Adaptive Control, with Pravin P. Varaiya.

The theoretical analysis is verified through the Monte-Carlo stochastic simulation studies. 1 Introduction Identification of stochastic systems with correlated noise is a much more formidable task.

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