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Traditional distance protections mainly use a lumped parameter model and perform poorly under high-resistance ground fault and/or for long-distance transmission line. In order to overcome the defects, this paper presents a new distance protection method, which is developed based on the distributed-parameter transmission-line model and can distinguish internal faults from external faults based on the...
We propose an optimal control framework for persistent monitoring problems where the objective is to control the movement of multiple cooperating agents to minimize an uncertainty metric in a given mission space. In a one-dimensional mission space, we show that the optimal solution is for each agent to move at maximal speed from one switching point to the next, possibly waiting some time at each point...
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...
We consider the problem of finding a control policy for a Markov Decision Process (MDP) to maximize the probability of reaching some states while avoiding some other states. This problem is motivated by applications in robotics, where such problems naturally arise when probabilistic models of robot motion are required to satisfy temporal logic task specifications. We transform this problem into a...
We propose an optimal control framework for persistent monitoring problems where the objective is to control the movement of mobile agents to minimize an uncertainty metric in a given mission space. For a single agent in a one-dimensional space, we show that the optimal solution is obtained in terms of a sequence of switching locations, thus reducing it to a parametric optimization problem. Using...
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