Stable receding-horizon scenario predictive control for Markov-jump linear systems

This paper discusses predictive control for constrained discrete-time Markov-jump linear systems (MJLS) which jump between a finite set of modes according to a Markov probabilistic transition/observation model, minimising an average cost. Due to the exponential explosion of the number of possible realisations as horizon grows, scenario approaches consider only a subset of them. Prior works cast the problem as a tree-based optimisation one, but enforce stability and feasibility via artificial Lyapunov-related constraints. The proposed approach avoids this route, proposing instead 'terminal ingredients' and tree properties (trim-contained, strictly-complete) properly generalising the stability/feasibility ideas in linear and MJLS literature.