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Dettaglio pubblicazione

2020, 2020 European Control Conference (ECC), Pages 29-34

Learning model predictive control for periodic repetitive tasks (04b Atto di convegno in volume)

Scianca Nicola, Rosolia Ugo, Borrelli Francesco

We propose a reference-free learning model predictive controller for periodic repetitive tasks. We consider a problem in which dynamics, constraints and stage cost are periodically time-varying. The controller uses the closed-loop data to construct a time-varying terminal set and a time-varying terminal cost. We show that the proposed strategy in closed loop with linear and nonlinear systems guarantees recursive constraints satisfaction, non-increasing open-loop cost, and that the open-loop and closed-loop cost are the same at convergence. Simulations are presented for different repetitive tasks, both for linear and nonlinear systems.
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