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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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Stochastic controllability and stochastic Lyapunov functions
Proceedings of the 27th IEEE Conference on Decision and Control, 2003Sufficient conditions are established under which the law of large numbers and related ergodic theorems hold for nonlinear stochastic systems operating under feedback. It is shown that these conditions hold whenever a moment condition is satisfied, which may be interpreted as a generalization of the martingale property.
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2021
The concept of a stochastic control system is defined as a map from a tuple of the current state and the current input to the conditional probability distribution of the tuple of the next state and the current output. A Gaussian stochastic control system representation is defined which represents such a stochastic system.
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The concept of a stochastic control system is defined as a map from a tuple of the current state and the current input to the conditional probability distribution of the tuple of the next state and the current output. A Gaussian stochastic control system representation is defined which represents such a stochastic system.
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2021
A stochastic control problem is to determine a control law within a rather general set of control laws such that the closed-loop system meets prespecified control objectives. A stochastic control problem is motivated by control problem of engineering, economics, or other areas of the sciences.
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A stochastic control problem is to determine a control law within a rather general set of control laws such that the closed-loop system meets prespecified control objectives. A stochastic control problem is motivated by control problem of engineering, economics, or other areas of the sciences.
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2021
Stochastic control issues of a general character are presented. Problems of control theory are mentioned which require research interest the coming years. A general method for sufficient and necessary conditions for the existence of an optimal control law is discussed. The framework covers arbitrary cost functions, including additive and multiplicative
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Stochastic control issues of a general character are presented. Problems of control theory are mentioned which require research interest the coming years. A general method for sufficient and necessary conditions for the existence of an optimal control law is discussed. The framework covers arbitrary cost functions, including additive and multiplicative
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2003
The general theory of stochastic processes originated in the fundamental works of A. N. Kolmogorov and A. Ya. Khincin at the beginning of the 1930s. Kolmogorov, 1938 gave a systematic and rigorous construction of the theory of stochastic processes without aftereffects or, as it is customary to say nowadays, Markov processes.
Viorel Arnăutu, Pekka Neittaanmäki
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The general theory of stochastic processes originated in the fundamental works of A. N. Kolmogorov and A. Ya. Khincin at the beginning of the 1930s. Kolmogorov, 1938 gave a systematic and rigorous construction of the theory of stochastic processes without aftereffects or, as it is customary to say nowadays, Markov processes.
Viorel Arnăutu, Pekka Neittaanmäki
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Stochastic Realization for Stochastic Control with Partial Observations
2007The purpose of this paper is to present a novel way to formulate control problems with partial observations of stochastic systems. Themethod is based on stochastic realization theory.
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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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1997
Methods of applied stochastic control may be viewed as extensions of the deterministic control approach in two ways. One is the characterization of the stochastic process over time that may generate the deterministic control model. The second is the estimation aspect where the parameters of the deterministic control model are assumed to be unknown, and
Jati K. Sengupta, Phillip Fanchon
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Methods of applied stochastic control may be viewed as extensions of the deterministic control approach in two ways. One is the characterization of the stochastic process over time that may generate the deterministic control model. The second is the estimation aspect where the parameters of the deterministic control model are assumed to be unknown, and
Jati K. Sengupta, Phillip Fanchon
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Mathematical Control Theory for Stochastic Partial Differential Equations
Probability Theory and Stochastic Modelling, 2021Qi Lü, Xu Zhang
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