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Advances in the Control of Markov Jump Linear Systems with No Mode Observation

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...

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Main Authors: Vargas, Alessandro N. (Author, http://id.loc.gov/vocabulary/relators/aut), Costa, Eduardo F. (http://id.loc.gov/vocabulary/relators/aut), do Val, João B. R. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:SpringerBriefs in Control, Automation and Robotics,
Subjects:
Control engineering.
System theory.
Probabilities.
Operator theory.
Control and Systems Theory.
Systems Theory, Control.
Probability Theory and Stochastic Processes.
Operator Theory.
Online Access:https://doi.org/10.1007/978-3-319-39835-8
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505 0 |a Preliminaries -- Finite-Time Control Problem -- Approximation of the Optimal Long-Run Average-Cost Control Problem -- References. 
520 |a 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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650 0 |a System theory. 
650 0 |a Probabilities. 
650 0 |a Operator theory. 
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