State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

Abstract

In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.

State estimation and nonlinear tracking control simulation approach. Application to a bioethanol production system

“…The methodology described in Fernández et al [19] aims to find U in order to make the system follow pre-established state Inhibition coefficient for cell growth on ethanol (g/l)…”

“…( 1) is discretized using numerical methods. Euler is applied due to its simplicity and good results [19].…”

“…This variable, defined as S 1ez , ensures that (7) has an exact solution. For more details on its selection and calculation, see [19]. Finally, U n is obtained using least squares.…”

“…Many scientists have developed several feedback control strategies. However, to the best of the author's knowledge, for the specific case of fed-batch bioethanol production defined by Hunag et al [18], only Fernández et al [19] presented a controller design. It is focused on looking for control actions, to track predefined concentration profiles.…”

“…With this alternative, in comparison with NN, fewer parameters are involved, simplifying the mathematical resolution of the problem, reducing the programming time and minimizing the computational cost. Furthermore, the controller parameters are selected with a Monte Carlo Randomized algorithm and a comparison with the NN state estimators, previously presented [19], is shown, under parametric uncertainties and external perturbation addition.…”