Control Design for Markov Chains under Safety Constraints: A Convex Approach

This paper focuses on the design of time-invariant memoryless control policies for fully observed controlled Markov chains, with a finite state space. Safety constraints are imposed through a pre-selected set of forbidden states. A state is qualified as safe if it is not a forbidden state and the pr...

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Veröffentlicht in:arXiv.org 2012-11
Hauptverfasser: Arvelo, Eduardo, Martins, Nuno C
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description This paper focuses on the design of time-invariant memoryless control policies for fully observed controlled Markov chains, with a finite state space. Safety constraints are imposed through a pre-selected set of forbidden states. A state is qualified as safe if it is not a forbidden state and the probability of it transitioning to a forbidden state is zero. The main objective is to obtain control policies whose closed loop generates the maximal set of safe recurrent states, which may include multiple recurrent classes. A design method is proposed that relies on a finitely parametrized convex program inspired on entropy maximization principles. A numerical example is provided and the adoption of additional constraints is discussed.
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subjects Closed loops
Markov analysis
Markov chains
Policies
Safety
title Control Design for Markov Chains under Safety Constraints: A Convex Approach
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