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Advances in the Control of Markov Jump Linear Systems with No Mode Observation [electronic resource] / by Alessandro N. Vargas, Eduardo F. Costa, João B. R. do Val.

By: Vargas, Alessandro N [author.].
Contributor(s): Costa, Eduardo F [author.] | do Val, João B. R [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Control, Automation and Robotics: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016.Description: V, 48 p. 8 illus., 6 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319398358.Subject(s): Control engineering | System theory | Control theory | Probabilities | Operator theory | Control and Systems Theory | Systems Theory, Control | Probability Theory | Operator TheoryAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 629.8312 | 003 Online resources: Click here to access online
Contents:
Preliminaries -- Finite-Time Control Problem -- Approximation of the Optimal Long-Run Average-Cost Control Problem -- References.
In: Springer Nature eBookSummary: This brief broadens readers’ understanding of stochastic control by highlighting recent advances in the design of optimal control for Markov jump linear systems (MJLS). It also presents an algorithm that attempts to solve this open stochastic control problem, and provides a real-time application for controlling the speed of direct current motors, illustrating the practical usefulness of MJLS. Particularly, it offers novel insights into the control of systems when the controller does not have access to the Markovian mode.
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Preliminaries -- Finite-Time Control Problem -- Approximation of the Optimal Long-Run Average-Cost Control Problem -- References.

This brief broadens readers’ understanding of stochastic control by highlighting recent advances in the design of optimal control for Markov jump linear systems (MJLS). It also presents an algorithm that attempts to solve this open stochastic control problem, and provides a real-time application for controlling the speed of direct current motors, illustrating the practical usefulness of MJLS. Particularly, it offers novel insights into the control of systems when the controller does not have access to the Markovian mode.

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