Abstract
This chapter considers the closed-loop properties of stochastic MPC strategies based on the predicted costs and probabilistic constraints formulated in Chap. 6. To make the analysis of closed-loop stability and performance possible, it must first be ensured that the MPC law is well-defined at all times and the most natural way to approach this is to ensure that the associated receding horizon optimization problem remains feasible whenever it is initially feasible . We therefore begin by discussing the conditions for recursive feasibility.
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Kouvaritakis, B., Cannon, M. (2016). Feasibility, Stability, Convergence and Markov Chains. In: Model Predictive Control. Advanced Textbooks in Control and Signal Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-24853-0_7
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DOI: https://doi.org/10.1007/978-3-319-24853-0_7
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