Electrical and Electronic Engineering - Research Publications
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ItemMoving Horizon Estimation for Linear Cascade Systems(IEEE, 2018-01-01)A structured approach to the problem of state estimation for cascaded linear sub-systems is studied in terms of minimizing a measure of the error relative to a model over a moving horizon of past system input and output observations. A quadratic programming formulation of this optimization problem is considered and two approaches are explored. One approach involves solving the Karush-Kuhn-Tucker conditions directly, and the other is based on the alternating direction method of multipliers. In both cases, the problem structure can be exploited to yield distributed computations in the following sense: Construction of the estimate for each sub-system component of the state involves information pertaining to the two immediate neighbours only. Numerical simulations based on model data from an automated irrigation channel are used to investigate and compare the computational burden of the two approaches.
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ItemStructured moving horizon estimation for linear system chains(IEEE, 2019-06-01)Computational aspects of moving horizon state estimation are studied for a class of chain networks with bidirectional coupling in the linear state dynamics, and measured outputs. Moving horizon estimation involves solving a quadratic program to minimize the estimation error relative to a model over a fixed window of past input-output observations. By exploiting the spatial structure of a chain, two algorithms for solving this quadratic program are considered. Both algorithms can be distributed in the sense that the computations associated with each sub-system component of the state depend only on information associated with the immediate neighbours. The algorithms differ in the way that the linear Karush-Kuhn-Tucker conditions for optimality are solved. Computational and information dependency overheads are analyzed. Numerical results are presented for a 1-D mass-spring-damper chain.
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ItemOptimal contract design for effort-averse sensors(Taylor & Francis, 2018-06-28)A central planner wishes to engage a collection of sensors to measure a quantity. Each sensor seeks to trade-off the effort it invests to obtain and report a measurement, against contracted reward. Assuming that measurement quality improves as a sensor increases the effort it invests, the problem of reward contract design is investigated. To this end, a game is formulated between the central planner and the sensors. Using this game, it is established that the central planner can enhance the quality of the estimate by rewarding each sensor based on the distance between the average of the received measurements and the measurement provided by the sensor. Optimal contracts are designed from the perspective of the budget required to achieve a specified level of error performance.
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ItemGeometric techniques for implicit two-dimensional systems(SPRINGER, 2013-12)
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ItemA note on robust stability analysis for feedback interconnections of time-varying linear systems(University of Minnesota, 2016)
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ItemComputing the distance between time-periodic dynamical systems(University of Minnesota, 2016)
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ItemPromoting Truthful Behavior in Participatory-Sensing Mechanisms(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2015-10)