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Stochastic Model Predictive Control using a combination of randomized and robust optimization
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Stochastic programming : the state of the art : in honor of George B. Dantzig
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Robust constrained model predictive control using linear matrix inequalities
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Optimization over state feedback policies for robust control with constraints
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Convexity and convex approximations of discrete-time stochastic control problems with constraints
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Systems with persistent disturbances: predictive control with restricted constraints
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Predictive control with constraints
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Min-max feedback model predictive control for constrained linear systems
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Randomized Model Predictive Control for stochastic linear systems
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A randomized approach to Stochastic Model Predictive Control
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A probabilistic framework for security constrained reserve scheduling of networks with wind power generation
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The Scenario Approach to Robust Control Design
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Stochastic Receding Horizon Control of Constrained Linear Systems With State and Control Multiplicative Noise
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Probabilistic Constrained MPC for Multiplicative and Additive Stochastic Uncertainty
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Robust Model Predictive Control via Scenario Optimization
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On the Road Between Robust Optimization and the Scenario Approach for Chance Constrained Optimization Problems
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Stochastic Model Predictive Control for Building Climate Control
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The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
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Randomized Solutions to Convex Programs with Multiple Chance Constraints
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Scenario-based MPC for energy-efficient building climate control under weather and occupancy uncertainty
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On convex problems in chance-constrained stochastic model predictive control
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Robust and Stochastic MPC: Are We Going In The Right Direction?
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Compression learning for chance constrained stochastic MPC**Research supported by the European Commission under the project SPEEDD. The authors would like to thank Prof. S. Garatti for stimulating discussionshttp://www.hamecmopsys.ens2m.fr/.
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Efficient River Management using Stochastic MPC and Ensemble Forecast of Uncertain In-flows ⁎ ⁎The first and the third authors acknowledge the financial support from the Australian Research Council Linkage Project (LP130100605) and the Brescia Smart Living Project (MIURSCN00416) respectively.
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