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


X. Zhang

ETH Zurich, Switzerland

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".


Type of Publication:

(03)Ph.D. Thesis

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},
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