Probability rate optimization of positive Markov jump linear systems via DC programming
Chengyan Zhao, Bohao Zhu, Masaki Ogura, James Lam
Abstract
Abstract We investigate the stabilization problem of positive Markov jump linear systems by optimizing their transition probability rates. By using the convex property of posynomials and the standard mathematical programming that deals with the difference in convex functions, we show that transition probability rate synthesis problems can be solved via difference‐of‐convex (DC) programming. A numerical example is used to illustrate the effectiveness of our results.
Topics & Concepts
Transition rate matrixMarkov chainJumpConvex optimizationLinear programmingMathematical optimizationMathematicsRegular polygonConvex combinationMarkov processConvex functionApplied mathematicsStatisticsGeometryQuantum mechanicsPhysicsStability and Control of Uncertain SystemsAdvanced Optimization Algorithms ResearchControl Systems and Identification