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Title:Estimation and control with limited information and unreliable feedback
Author(s):Sharon, Yoav
Director of Research:Liberzon, Daniel M.; Ma, Yi
Doctoral Committee Chair(s):Liberzon, Daniel M.
Doctoral Committee Member(s):Ma, Yi; Hovakimyan, Naira; Coleman, Todd P.
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Quantized systems
Stability of hybrid systems
Input-to-state stability (ISS)
Output feedback and Observers
Control with communication constraints
Time delays
Robust Estimation
Compressed Sensing
Vision Based Control
Abstract:Advancement in sensing technology is introducing new sensors that can provide information that was not available before. This creates many opportunities for the development of new control systems. However, the measurements provided by these sensors may not follow the classical assumptions from the control literature. As a result, standard control tools fail to maximize the performance in control systems utilizing these new sensors. In this work we formulate new assumptions on the measurements applicable to new sensing capabilities, and develop and analyze control tools that perform better than the standard tools under these assumptions. Specifically, we make the assumption that the measurements are quantized. This assumption is applicable, for example, to low resolution sensors, remote sensing using limited bandwidth communication links, and vision-based control. We also make the assumption that some of the measurements may be faulty. This assumption is applicable to advanced sensors such as GPS and video surveillance, as well as to remote sensing using unreliable communication links. The first tool that we develop is a dynamic quantization scheme that makes a control system stable to any bounded disturbance using the minimum number of quantization regions. Both full state feedback and output feedback are considered, as well as nonlinear systems. We further show that our approach remains stable under modeling errors and delays. The main analysis tool we use for proving these results is the nonlinear input-to-state stability property. The second tool that we analyze is the Minimum Sum of Distances estimator that is robust to faulty measurements. We prove that this robustness is maintained when the measurements are also corrupted by noise, and that the estimate is stable with respect to such noise. We also develop an algorithm to compute the maximum number of faulty measurements that this estimator is robust to. The last tool we consider is motivated by vision-based control systems. We use a nonlinear optimization that is taking place over both the model parameters and the state of the plant in order to estimate these quantities. Using the example of an automatic landing controller, we demonstrate the improvement in performance attainable with such a tool.
Issue Date:2011-01-21
Rights Information:2010 Yoav Sharon
Date Available in IDEALS:2013-01-22
Date Deposited:2010-12

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