Learning Model Predictive Control for Periodic Repetitive Tasks

Nicola Scianca; Ugo Rosolia; Francesco Borrelli

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arXiv · 1911.07535

Learning Model Predictive Control for Periodic Repetitive Tasks

Nicola Scianca·Ugo Rosolia·Francesco Borrelli

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Abstract

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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Nicola Scianca, Ugo Rosolia, Francesco Borrelli. 2019-11-18. Learning Model Predictive Control for Periodic Repetitive Tasks. https://arxiv.org/abs/1911.07535

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