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Title:Self-Tuning Methods for Multiple-Controller Systems
Author(s):Chan, Yick Man
Department / Program:Electrical Engineering
Discipline:Electrical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Engineering, Electronics and Electrical
Abstract:The optimization of stochastic systems with unknown parameters and multiple decision-makers or controllers each having his own objective is considered. Based on a centralized information pattern, steady-state solutions are obtained for the stochastic adaptive Nash game and Leader-Follower game problems. These adaptive solutions, after a judicious transformation, resemble closely the implicit self-tuning solution for the single-controller single-objective case, and thus preserve the salient and advantageous features of self-tuning methods--simplicity and easy implementation. In addition, due to this close resemblance, convergence results for the game problems are established by extending the convergence result from the single-controller single-objective case. The decentralized stochastic adaptive Nash game problem is also considered. Two explicit self-tuning type algorithms are proposed. The first algorithm is an ad hoc constraint on the policy form while the second one is based on extension from static Nash game theory. Simulation results indicate all these self-tuning methods are capable of stabilizing a system along targeted paths.
Issue Date:1981
Type:Text
Language:English
Description:110 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1981.
URI:http://hdl.handle.net/2142/66258
Other Identifier(s):(UMI)AAI8203422
Date Available in IDEALS:2014-12-12
Date Deposited:1981


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