作者: Emil Petre , Dan Selişteanu , Dorin Şendrescu
DOI: 10.1007/978-3-642-22194-1_21
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摘要: This work deals with the design and analysis of some nonlinear neural adaptive control strategy for a lactic acid production that is carried out in continuous stirred tank bioreactors. An indirect controller based on dynamical network used as on-line approximator to learn time-varying characteristics process parameters developed then compared classical linearizing controller. The achieved by using an input-output feedback linearization technique. effectiveness performance both algorithms are illustrated numerical simulations applied case fermentation bioprocess which kinetic dynamics strongly nonlinear, time varying completely unknown.