Parameters optimization applying monte carlo methods and evolutionary algorithms. Enforcement to a trajectory tracking controller in non-linear systems Optimización de parámetros utilizando los métodos de monte carlo y algoritmos evolutivos. Aplicación a un controlador de seguimiento de trayectoria en sistemas no lineales

FERNÁNDEZ, C.; PANTANO, N.; GODOY, S.; SERRANO, E.; SCAGLIA, G.

REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL

COMITE ESPANOL AUTOMATICA CEA

Año: 2019 vol. 16 p. 89 - 99

1697-7912

In this work, a closed-loop control strategy is proposed. It allows tracking optimal profiles for a fed-batch bioprocess. The main advantage of this approach is that the control actions are computed from a linear equations system without linearizing the mathematical model, which allows working in any range. In addition, three techniques are developed to tune the controller. First, a completely probabilistic method, Monte Carlo. Second, a methodology based on Genetic Algorithms, an evolutionary optimization technique. Third, a Hybrid Algorithm, combining above algorithms advantages. Here, the objective function is to find the controller parameters that minimize the trajectory tracking total error. The controller performance is evaluated through simulations under normal operations conditions and parametric uncertainty, using the obtained controller parameters.