A Pontryagin-based approach to solve a class of constrained Nonlinear Model Predictive Control problems is proposed, which employs the method of penalty functions for dealing with the state constraints. Unlike the existing works in literature, the proposed method is able to cope with nonlinear input and state constraints without any significant modification of the optimization algorithm. Theoretical results are tested and confirmed by numerical simulations on the Lotka-Volterra prey/predator nonlinear system.

A penalty function approach to constrained Pontryagin-based Nonlinear Model Predictive Control / Pagone, Michele; Boggio, Mattia; Novara, Carlo; Proskurnikov, Anton; Calafiore, GIUSEPPE CARLO. - ELETTRONICO. - (2022). ((Intervento presentato al convegno IEEE Conference on Decision and Control tenutosi a Cancún, Mexico nel 6-9 December, 2022 [10.1109/CDC51059.2022.9992438].

A penalty function approach to constrained Pontryagin-based Nonlinear Model Predictive Control

Michele Pagone;Mattia Boggio;Carlo Novara;Anton Proskurnikov;Giuseppe Calafiore
2022

Abstract

A Pontryagin-based approach to solve a class of constrained Nonlinear Model Predictive Control problems is proposed, which employs the method of penalty functions for dealing with the state constraints. Unlike the existing works in literature, the proposed method is able to cope with nonlinear input and state constraints without any significant modification of the optimization algorithm. Theoretical results are tested and confirmed by numerical simulations on the Lotka-Volterra prey/predator nonlinear system.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2971274