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Adaptive Stochastic MPC under Time Varying Uncertainty 2020 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/8173 https://ieeexplore.ieee.org/abstract/document/9141508/citations#citations M. Bujarbaruah X. Zhang M. Tanasković F. Borrelli This paper deals with the problem of formulating an adaptive Model Predictive Control strategy for constrained uncertain systems. We consider a linear system, in presence of bounded time varying additive uncertainty. The uncertainty is decoupled as the sum of a process noise with known bounds, and a time varying offset that we wish to identify. The time varying offset uncertainty is assumed unknown point-wise in time. Its domain, called the Feasible Parameter Set, and its maximum rate of change are known to the control designer. As new data becomes available, we refine the Feasible Parameter Set with a Set Membership Method based approach, using the known bounds on process noise. We consider the case of probabilistic constraints on system states, with hard constraints on actuator inputs. We robustly satisfy the imposed constraints for all possible values of the offset uncertainty in the Feasible Parameter Set. By imposing adequate terminal conditions, we prove recursive feasibility and stability of the proposed algorithm. The efficacy of the proposed approach is illustrated with a detailed numerical example. article 10.1109/TAC.2020.3009362 1 1 0018-9286 IEEE TRANSACTIONS ON AUTOMATIC CONTROL