Browsing by Author "Lutz, Collin C."

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    Environmental Information Improves Robotic Search Performance
    Yetkin, Harun; Lutz, Collin C.; Stilwell, Daniel J. (Virginia Tech, 2016)
    We address the problem where a mobile search agent seeks to find an unknown number of stationary objects distributed in a bounded search domain, and the search mission is subject to time/distance constraint. Our work accounts for false positives, false negatives and environmental uncertainty. We consider the case that the performance of a search sensor is dependent on the environment (e.g., clutter density), and therefore sensor performance is better in some locations than in others. For applications where environmental information can be acquired, we derive a decision-theoretic cost function to compute the locations where the environmental information should be acquired. We address the cases where environmental characterization is performed either by a separate vehicle or by the same vehicle that performs the search task.
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    Switched Markov Jump Linear Systems: Analysis and Control Synthesis
    Lutz, Collin C. (Virginia Tech, 2014-11-14)
    Markov jump linear systems find application in many areas including economics, fault-tolerant control, and networked control. Despite significant attention paid to Markov jump linear systems in the literature, few authors have investigated Markov jump linear systems with time-inhomogeneous Markov chains (Markov chains with time-varying transition probabilities), and even fewer authors have considered time-inhomogeneous Markov chains with a priori unknown transition probabilities. This dissertation provides a formal stability and disturbance attenuation analysis for a Markov jump linear system where the underlying Markov chain is characterized by an a priori unknown sequence of transition probability matrices that assumes one of finitely-many values at each time instant. Necessary and sufficient conditions for uniform stochastic stability and uniform stochastic disturbance attenuation are reported. In both cases, conditions are expressed as a set of finite-dimensional linear matrix inequalities (LMIs) that can be solved efficiently. These finite-dimensional LMI analysis results lead to nonconservative LMI formulations for optimal controller synthesis with respect to disturbance attenuation. As a special case, the analysis also applies to a Markov jump linear system with known transition probabilities that vary in a finite set.