Self-Tuning Methods for Multiple-Controller Systems
Chan, Yick Man
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https://hdl.handle.net/2142/66258
Description
Title
Self-Tuning Methods for Multiple-Controller Systems
Author(s)
Chan, Yick Man
Issue Date
1981
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
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.
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