Adaptive Control of Markov Chains: An Optimization Oriented Approach (Queueing Networks, Stochastic Models, Computer)

In this thesis we consider the control of a dynamic system modeled as a Markov chain. The transition probability matrix of the Markov chain depends on the control u and also on an unknown parameter (alpha)('o). The unknown parameter belongs to a given finite set A. The performance of the system is measured by a long run average cost criterion. A direct approach to the optimization of the performance is not feasible. A common procedure calls for an on-line estimation of the unknown parameter and the minimization of the cost functional using the estimate in lieu of the true parameter. This certainty equivalence (CE) solution may fail to achieve optimal performance.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Milito, Rodolfo Alberto

• Engineering, Electronics and Electrical

Identifier

(UMI)AAI8511646

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/69302