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We present a data-driven model predictive control scheme forchance-constrained Markovian switching systems with unknown switchingprobabilities. Using samples of the underlying Markov chain, ambiguity sets oftransition probabilities are estimated which include the true conditionalprobability distributions with high probability. These sets are updated onlineand used to formulate a time-varying, risk-averse optimal control problem. Weprove recursive feasibility of the resulting MPC scheme and show that theoriginal chance constraints remain satisfied at every time step. Furthermore,we show that under sufficient decrease of the confidence levels, the resultingMPC scheme renders the closed-loop system mean-square stable with respect tothe true-but-unknown distributions, while remaining less conservative than afully robust approach.

Learning-Based Distributionally Robust Model Predictive Control of Markovian Switching Systems with Guaranteed Stability and Recursive Feasibility