A New Approach for Nonlinear Multivariable Fed-Batch Bioprocess Trajectory Tracking Control

Abstract

⎯This paper proposes a new control law based on linear algebra. This technique allows nonlinear path tracking in multivariable and complex systems. This new methodology consists in finding the control action to make the system follow predefined concentration profiles solving a system of linear equations. The controller parameters are selected with a Monte Carlo algorithm so as to minimize a previously defined cost index. The control scheme is applied to a fed-batch penicillin production process. Different tests are shown to prove the controller effectiveness, such as adding parametric uncertainty, perturbations in the control action and in the initial conditions. Moreover, a comparison with other controllers from the literature is made, showing the better performance of the present approach.

Control de trayectorias basado en álgebra lineal

“…Correlación entre las prestaciones del sistema controlado y los parámetros de control Análisis del efecto de observadores Análisis e influencia del periodo de muestreo en la implementación del control Presencia de saturación en las señales de control Consideración de otro tipo de perturbaciones Influencia de las acciones integrales en la respuesta transitoria Diseño del control para sistemas con dinámica cero inestable Ampliar el campo de aplicaciones al control de otros procesos dinámicos Finalmente es de resaltar que esta metodología ha sido ya aplicada en diversos tipos de procesos, como puede verse en las referencias que se indican (Scaglia et al (2015); Serrano et al (2013); Pantano et al (2018Pantano et al ( , 2019; Fernández et al (2018); Rosales et al (2015); Gandolfo et al (2014)).…”

Multivariable Tracking Control of a Bioethanol Process under Uncertainties

“…On the other hand, many scientists have developed several feedback control strategies to deal with bioprocess uncertainties, such as optimal control, nonlinear model predictive control, fuzzy control, hybrid control, adaptive control, tracking control, and neural network [37,[39][40][41][42][43]. Due to the online implementation difficulty, the high computational cost, the imprecise mathematical models, and online solutions, their use for bioprocesses is limited [17] and is still a research topic [44].…”

Tracking Control of Optimal Profiles in a Nonlinear Fed-Batch Bioprocess under Parametric Uncertainty and Process Disturbances

“…The main technique advantage is that control actions computed are obtained solving linear system equations, ensuring the convergence to zero of tracking errors. Besides, that control technique has been applied successfully in several systems, ,− and therefore has a high potential for applicability in all bioprocess environments.…”