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In this paper we study the problem of scheduling sensors to estimate the state of a linear dynamical system. The estimator is a Kalman filter and we seek to optimize the a posteriori error covariance over an infinite time horizon. We characterize the exact conditions for the existence of a schedule with uniformly bounded estimation error covariance. Using this result, we construct a scheduling algorithm...
We propose a linear programming-based method of interval observer design for systems with uncertain but bounded model parameters and initial conditions. We assume that each uncertain parameter in the system model is bounded by conservative guaranteed bounds, and tighter conditional bounds. We define a class of systems by the number of conservative bounds required to bound all uncertain parameters...
In this paper, we develop a method to automatically generate a control policy for a dynamical system modeled as a Markov Decision Process (MDP). The control specification is given as a Linear Temporal Logic (LTL) formula over a set of propositions defined on the states of the MDP. Motivated by robotic applications requiring persistent tasks, such as environmental monitoring and data gathering, we...
In this paper, we develop a method to automatically generate a control policy for a dynamical system modeled as a Markov Decision Process (MDP). The control specification is given as a Linear Temporal Logic (LTL) formula over a set of propositions defined on the states of the MDP. We synthesize a control policy such that the MDP satisfies the given specification almost surely, if such a policy exists...
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