This paper aims to solve the problem
of tracking optimal profiles
for a nonlinear multivariable fed-batch bioprocess by a simple but
efficient closed-loop control technique based on a linear algebra
approach. In the proposed methodology, the control actions are obtained
by solving a system of linear equations without the need for state
transformations. The optimal profiles to follow are directly those
corresponding to output desired variables, therefore, estimation of
states for nonmeasurable variables is considered by employing a neural
networks method. The efficiency of the proposed controller is tested
through several simulations, including process disturbances and operation
under parametric uncertainty. The optimal controller parameters are
selected through the Montecarlo Randomized Algorithm. In addition,
proof of convergence to zero of tracking errors is analyzed and included
in this article.
The problem of optimal profiles tracking control under uncertainties for a nonlinear fed-batch bioprocess is addressed in this paper. Based on the results reported by Pantano et al. [Ind. Eng. Chem. Res. 2017, 56, 6043], this work aims to improve the control system response against parametric uncertainty and process disturbances. The methodology is simple and easy to implement, but with excellent results. The design parameters are optimized by a randomized Monte Carlo algorithm. Besides, demonstration of the tracking error convergence to zero when the system is subjected to uncertainties is included in the article. The control system performance is tested through simulations, showing the improvement achieved.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.