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Stochastic Optimal Control Subject to Ambiguity
IFAC Proceedings Volumes, 2011The aim of this paper is to address optimality of control strategies for stochastic control systems subject to uncertainty and ambiguity. Uncertainty corresponds to the case when the true dynamics and the nominal dynamics are dierent but they are dened on the same state space.
Charalambous, Charalambos D. +5 more
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Optimal Control Problem of Stochastic Systems
Lobachevskii Journal of Mathematics, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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1998
Abstract This chapter gives a self‐contained introduction to optimal control of stochastic differential equations. We derive the Hamilton‐Jacobi‐Bellman equation as well as a verification theorem. The general theory is then applied to optimal consumption and investment problems.
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Abstract This chapter gives a self‐contained introduction to optimal control of stochastic differential equations. We derive the Hamilton‐Jacobi‐Bellman equation as well as a verification theorem. The general theory is then applied to optimal consumption and investment problems.
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Stochastic Optimal Control Problems
1999Uncertainty is inherent in most real-world systems. It places many disadvantages (and sometimes, surprisingly, advantages) on humankind’s efforts, which are usually associated with the quest for optimal results. The systems mainly studied in this book are dynamic, namely, they evolve over time.
Jiongmin Yong, Xun Yu Zhou
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1971
So far we have applied the Kalman filter to systems which were subjected to random disturbances but were not controlled. Very briefly we turn now our attention to the case were we wish to employ measurements to control a system in some optimal manner. Only the simplest problem will be discussed here.
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So far we have applied the Kalman filter to systems which were subjected to random disturbances but were not controlled. Very briefly we turn now our attention to the case were we wish to employ measurements to control a system in some optimal manner. Only the simplest problem will be discussed here.
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Stochastic optimal open-loop feedback control
Advances in Engineering Software, 2011Considering a dynamic control system with random model parameters and using the stochastic Hamilton approach stochastic open-loop feedback controls can be determined by solving a two-point boundary value problem (BVP) that describes the optimal state and costate trajectory. In general an analytical solution of the BVP cannot be found.
K. Marti, I. Stein
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2011
This chapter deals with the optimal control of a noisy linear system, the state of which is not entirely available, i.e., which requires a state reconstructor in the control loop. Since the system is submitted to random influences, a filter, e.g. an optimal filter such as the Kalman filter, will be used.
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This chapter deals with the optimal control of a noisy linear system, the state of which is not entirely available, i.e., which requires a state reconstructor in the control loop. Since the system is submitted to random influences, a filter, e.g. an optimal filter such as the Kalman filter, will be used.
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On Stochastic Optimal Control in Ferromagnetism
Archive for Rational Mechanics and Analysis, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thomas Dunst +3 more
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Stochastic Linear Quadratic Optimal Control Problems
Applied Mathematics & Optimization, 2001The stochastic linear quadratic optimal control problem is extensively studied for the case of random coefficients, what is highly important in applications like mathematical finance (mean variance hedging). The cost functional is allowed to have a negative weight on the square of the control which reveals interesting differences to the deterministic ...
Chen, S., Yong, J.
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Pathwise Stochastic Optimal Control
SIAM Journal on Control and Optimization, 2007This paper approaches optimal control problems for discrete-time controlled Markov processes by representing the value of the problem in a dual Lagrangian form; the value is expressed as an infimum over a family of Lagrangian martingales of an expectation of a pathwise supremum of the objective adjusted by the Lagrangian martingale term.
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