Robust Constrained Model Predictive Control of Irrigation Systems Based on Data-Driven Uncertainty Set Constructions

A unified framework for adjustable robust optimization with endogenous uncertainty

“…However, traditional flexibility analysis focuses primarily on nonlinear problems, which may be the reason why it so far has not been recognized or noticed by the operations research community. In the last few years, the number of ARO-related works in PSE has increased rapidly, addressing diverse applications in process design, 19,20 planning and scheduling, [14][15][16][21][22][23][24] model predictive control, [25][26][27] supply chain optimization, 28,29 and so forth.…”

Distributed stochastic MPC for linear systems with probabilistic constraints and quantisation

“…Compared with robust MPC (RMPC), SMPC not only takes into account the stochastic characteristics of the uncertainties but also allows violations of constraints, although such violations are required to satisfy probabilistic constraints [1–3]. An important property of probabilistic constraints is that it enables SMPC to contain the tradeoff between the satisfaction of constraints and control performance, thus, the conservatism of RMPC [4–7] can be reduced.…”

A Unified Framework for Adjustable Robust Optimization with Endogenous Uncertainty

“…However, traditional flexibility analysis focuses primarily on nonlinear problems, which may be the reason why it so far has not been recognized or noticed by the operations research community. In the last few years, the number of ARO-related works in PSE has increased rapidly, addressing diverse applications in process design 19,20 , planning and scheduling [14][15][16][21][22][23][24] , model predictive control [25][26][27] , supply chain optimization 28,29 , etc.…”