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Adaptive distributed stochastic deep reinforcement learning control for voltage and frequency restoration in islanded AC microgrids with communication noise and delay. [PDF]
Sci RepDehkordi NM, Nekoukar V.
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Optimal reservoir operation using stochastic model predictive control
, 2016 Reetik Kumar Sahu
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Stochastic optimal structural control: Stochastic optimal open-loop feedback control
Advances in Engineering Software, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Pathwise Optimality in Stochastic Control
SIAM Journal on Control and Optimization, 2000This paper deals with the pathwise optimality for stochastic control problems over an infinite time horizon. The authors considered the following problems. For an admissible control \(u_t\) and its response \(x^u_t\), the running cost is given by \(J_T(u)=\int^T_0 c(x^u_t,u_t)dt\).
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1987
In the long history of mathematics, stochastic optimal control is a rather recent development. Using Bellman’s Principle of Optimality along with measure-theoretic and functional-analytic methods, several mathematicians such as H. Kushner, W. Fleming, R. Rishel. W.M. Wonham and J.M.
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In the long history of mathematics, stochastic optimal control is a rather recent development. Using Bellman’s Principle of Optimality along with measure-theoretic and functional-analytic methods, several mathematicians such as H. Kushner, W. Fleming, R. Rishel. W.M. Wonham and J.M.
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1970
H. J. Kushner has obtained the differential equation satisfied by the optimal feedback control law for a stochastic control system in which the plant dynamics and observations are perturbed by independent additive Gaussian white noise processes.
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H. J. Kushner has obtained the differential equation satisfied by the optimal feedback control law for a stochastic control system in which the plant dynamics and observations are perturbed by independent additive Gaussian white noise processes.
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2018
In previous chapters we assumed that the state variables of the system are known with certainty. When the variables are outcomes of a random phenomenon, the state of the system is modeled as a stochastic process. Specifically, we now face a stochastic optimal control problem where the state of the system is represented by a controlled stochastic ...
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In previous chapters we assumed that the state variables of the system are known with certainty. When the variables are outcomes of a random phenomenon, the state of the system is modeled as a stochastic process. Specifically, we now face a stochastic optimal control problem where the state of the system is represented by a controlled stochastic ...
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Automatica, 1969
It is indicated that optimal stochastic control is still in its infancy, and that at the present time it has little use in practice although a wide class of problems can be precisely stated. A brief survey of the problem involved in attempting to formulate and to solve optimal stochastic control problems is discussed along with the corresponding ...
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It is indicated that optimal stochastic control is still in its infancy, and that at the present time it has little use in practice although a wide class of problems can be precisely stated. A brief survey of the problem involved in attempting to formulate and to solve optimal stochastic control problems is discussed along with the corresponding ...
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