A Convex Feasibility Approach to Anytime Model Predictive Control

This paper proposes to decouple performance optimization and enforcement of asymptotic convergence in Model Predictive Control (MPC) so that convergence to a given terminal set is achieved independently of how much performance is optimized at each sampling step. By embedding an explicit decreasing condition in the MPC constraints and thanks to a novel and very easy-to-implement convex feasibility solver proposed in the paper, it is possible to run an outer performance optimization algorithm on top of the feasibility solver and optimize for an amount of time that depends on the available CPU resources within the current sampling step (possibly going open-loop at a given sampling step in the extreme case no resources are available) and still guarantee convergence to the terminal set. While the MPC setup and the solver proposed in the paper can deal with quite general classes of functions, we highlight the synthesis method and show numerical results in case of linear MPC and ellipsoidal and polyhedral terminal sets.Comment: 8 page

Domino alkylation-cyclization reaction of propargyl bromides with thioureas/thiopyrimidinones: A new facile synthesis of 2-aminothiazoles and 5H-thiazolo[3,2-a]pyrimidin-5-ones

A new synthesis of 2-aminothiazoles and 5H-thiazolo[3,2-a]pyrimidin-5-ones was developed as a domino alkylation-cyclization reaction of propargyl bromides with thioureas and thio¬pyrimidinones, respectively. Domino reactions were performed under microwave irradiation leading to desired compounds in a few minutes and high yield

Energy-aware robust model predictive control based on noisy wireless sensors

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Stabilizing Model Predictive Control of Stochastic Constrained Linear Systems

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