Results 301 to 310 of about 4,395,268 (339)
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The Optimal Control of a Train
Annals of Operations Research, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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IFAC Proceedings Volumes, 1965
The paper treats a method for developing a control strategy for a continuously operating nonlinear process. The strategy is shown to be optimal in the steady state and for small disturbances. It is near optimal for larger disturbances.
A.B. Aune, Jens G. Balchen
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The paper treats a method for developing a control strategy for a continuously operating nonlinear process. The strategy is shown to be optimal in the steady state and for small disturbances. It is near optimal for larger disturbances.
A.B. Aune, Jens G. Balchen
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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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2018
This chapter considers LQG optimal controls for input and state delayed systems. LQG controls use output feedback ones while LQ controls in the previous chapter require state feedback ones. State observers or filtered estimates are obtained from inputs and outputs to be used for LQG controls. First, finite horizon LQG controls are dealt with, which are
PooGyeon Park+2 more
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This chapter considers LQG optimal controls for input and state delayed systems. LQG controls use output feedback ones while LQ controls in the previous chapter require state feedback ones. State observers or filtered estimates are obtained from inputs and outputs to be used for LQG controls. First, finite horizon LQG controls are dealt with, which are
PooGyeon Park+2 more
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Optimal Control and Dynamic Optimization
2003Optimal control problems involve vector decision variables. These problems are one of the most mathematically challenging problems in optimization theory.
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Fuzzy Sets and Systems, 1992
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dimiter Filev, Plamen Angelov
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dimiter Filev, Plamen Angelov
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On the existence of optimal controls [PDF]
Using a recent result due to Berkovitz, we prove the existence of an optimal control in a broad class of problems, under relatively mild conditions.
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2015
Considering the foundations, tools, and emerging discoveries of collaborative e-Work, as discussed in Chapters 1 and 2, it is realized that optimization and control are focused primarily on the core elements of e-Systems; agents, protocols, and workflows.
Jose Ceroni+3 more
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Considering the foundations, tools, and emerging discoveries of collaborative e-Work, as discussed in Chapters 1 and 2, it is realized that optimization and control are focused primarily on the core elements of e-Systems; agents, protocols, and workflows.
Jose Ceroni+3 more
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Applied Mathematics and Optimization, 2002
The paper considers time optimal or \(L_1\)-norm (with respect to the state) optimal control problems for a linear parabolic equation with right-hand side as a measure valued control which drives the initial state to a prescribed target state. Necessary and sufficient conditions for optimality in the form of the maximum principle are derived.
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The paper considers time optimal or \(L_1\)-norm (with respect to the state) optimal control problems for a linear parabolic equation with right-hand side as a measure valued control which drives the initial state to a prescribed target state. Necessary and sufficient conditions for optimality in the form of the maximum principle are derived.
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Controllability and Optimization
2003We consider n players P i , i = 1,…, n, that are involved in a so called dynamical game. We assume that every player is assigned a state vector function x i : ℕ0 → ℝ ni and has at his disposal a control vector function u i : ℕ0 → ℝ mi which are dynamically coupled by a system of difference equations $$ \begin{gathered} x_i (t + 1) = g_i (x(t), u(t))
Stefan Pickl, Werner Krabs
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