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Inverse optimal neural control via passivity approach for nonlinear anaerobic bioprocesses with biofuels production
Author(s) -
Gurubel Kelly J.,
Sanchez Edgar N.,
CoronadoMendoza Alberto,
ZunigaGrajeda Virgilio,
SulbaranRangel Belkis,
BretonDeval Luz
Publication year - 2019
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2513
Subject(s) - control theory (sociology) , artificial neural network , nonlinear system , passivity , optimal control , trajectory , inverse , engineering , mathematics , computer science , mathematical optimization , control (management) , artificial intelligence , physics , geometry , quantum mechanics , astronomy , electrical engineering
Summary This paper proposes an inverse optimal neural control method of a nonlinear anaerobic bioprocesses model for simultaneous hydrogen and methane production in presence of disturbances. Based on the fundamental properties of the system, a passivity approach is designed such that asymptotic stability is guaranteed. A recurrent high‐order neural network for unknown nonlinear systems in presence of unknown bounded disturbances and parameter uncertainties is proposed to identify nonmeasurable state variables of the system, which are directly related to biofuels production. Optimal control laws based on the neural model are proposed so that the passivation of the entire plant is preserved. The neural control strategy performance for trajectory tracking in presence of disturbances is proven. Results via simulation show the optimal control methodology efficiency to stabilize the H 2 and CH 4 productions along desired trajectories even in presence of disturbances.