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Robust and Stochastic Control of Uncertain Systems: From Scenario Optimization to Adjustable Uncertainty Sets

Author(s):

X. Zhang
Conference/Journal:

ETH Zurich, Switzerland
Abstract:

The main theme of this thesis is the development of methods for synthesizing controllers for constrained uncertain systems. Generally speaking, two control paradigms exist for addressing uncertain systems: robust control and stochastic control. Strategies based on robust control treat all elements within a given uncertainty set equally, and aim at satisfying a given performance criterion (e.g., stability and constraint satisfaction) for all possible uncertainty realizations. In contrast, stochastic control strategies assume a probabilistic description of the uncertainty that allows us to optimize the performance of a controller towards events with a high probability of occurrence, while neglecting unlikely events.

Synthesizing robust and stochastic controllers for uncertain systems is often more challenging than for deterministic systems; in many cases, approximations must be made to achieve computational tractability. The challenge to efficiently design robust and stochastic controllers motivates the work of this thesis and defines its two parts: the first part is concerned with randomized algorithms as a method to synthesize stochastic controllers in a tractable manner, while the second part of this thesis addresses a novel class of problems called "robust optimal control problems with adjustable uncertainty sets".

Year:

2016
Type of Publication:

(03)Ph.D. Thesis
Supervisor:

J. Lygeros

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% Autogenerated BibTeX entry
@PhDThesis { Xxx:2016:IFA_5594,
    author={X. Zhang},
    title={{Robust and Stochastic Control of Uncertain Systems: From
	  Scenario Optimization to Adjustable Uncertainty Sets}},
    school={ETH Zurich},
    year={2016},
    address={},
    month=nov,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=5594}
}
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