Santiago Rómoli

Tracking control of concentration profiles in a fed-batch bioreactor using a linear algebra methodology

Based on a linear algebra approach, this paper aims at developing a novel control law able to track reference profiles that were previously-determined in the literature. A main advantage of the proposed strategy is that the control actions are obtained by solving a system of linear equations. The optimal controller parameters are selected through Monte Carlo Randomized Algorithm in order to minimize a proposed cost index. The controller performance is evaluated through several tests, and compared with other controller reported in the literature. Finally, a Monte Carlo Randomized Algorithm is conducted to assess the performance of the proposed controller.

Nonlinear control of the dissolved oxygen concentration integrated with a biomass estimator for production of Bacillus thuringiensis δ-endotoxins

Abstract Bacillus thuringiensis is a microorganism that allows the biosynthesis of δ-endotoxins with toxic properties against some insect larvae, being often used for the production of biological insecticides. A key issue for the bioprocess design consists in adequately tracking a pre-specified optimal profile of the dissolved oxygen concentration. To this effect, this paper aims at developing a novel control law based on a nonlinear dynamic inversion method. The closed-loop strategy includes an observer based on a Bayesian Regression with Gaussian Process, which is used for on-line estimating the biomass present in the bioreactor. Unlike other approaches, the proposed controller leads to an improved response time with effective disturbance rejection properties, while simultaneously prevents undesired oscillations of the dissolved oxygen concentration. Simulation results based on available experimental data were used to show the effectiveness of the proposal.

Control of a Fed-Batch Fermenter Based on a Linear Algebra Strategy

Based on a linear algebra approach, this paper aims at developing a novel control law able to track reference profiles that were previously-determined to optimize the protein production in a fed-batch fermenter. A main advantage of the proposed strategy is that the control actions are obtained by solving a system of linear equations. The optimal controller parameters are selected through Monte Carlo Experiments in order to minimize a proposed cost index. The controller performance is evaluated through several tests, and compared with other controller reported in the literature.

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

Trajectory tracking of boiler-turbine

This paper presents the design of a controller for trajectory tracking in a boiler-turbine system. It is a MIMO system with state variables strongly coupled. The proposed controller is simple and based on concepts easy to understand. Its design consists in representing the model while using numerical methods, and then the control actions are computed through the linear algebra techniques to accomplish the target tracking. The advantage of this method is that, the condition for the tracking error tends to zero and the calculation of control actions, are obtained by solving a system of linear equations. Besides, the method has the ability to follow trajectories for two outputs simultaneously.

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

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.

Controller design for tracking paths in nonlinear biochemical processes

The control of fed-batch bioprocess is a current challenge. Mathematical models are highly rigid systems of nonlinear differential equations with strict physical limitations. In this paper a simple and efficient technique for tracking optimal profiles with minimal error is developed. It is based on linear algebra for the calculation of control actions, by solving a system of linear equations. The performance of the designed controller is tested through simulations (adding parametric uncertainty and perturbations in the initial conditions), which show very satisfactory results.

Controller design by monitoring desired concentration profiles for the penicillin production in a feed batch reactor

The objective of this work is to design a controller for process variables for the penicillin production, carried out in a fed-batch reactor, following predefined profiles. The technique is based on linear algebra, which allows the design of multivariable controllers and highly nonlinear systems. To achieve this, it is necessary to possess a mathematical model that adequately represents the process and the concentration profiles that the system should follow. Simulation results are shown for different initial conditions.

Tracking multivariable optimal profiles of induced foreign protein production by recombinant bacteria in a fed-batch reactor

Bioprocesses are difficult to control, especially if one considers the fed-batch operation with more than one variable of control. Mathematical models include stiff and nonlinear differential equations, which are complex when designing a suitable driver for the system. This paper aims to implement a simple but efficient technique based on a linear algebra approach that allows an adequate tracking of optimal profiles of state variables making easier the design of the control system with very satisfactory results. The biological system presented has two control variables and two manipulated variables. The performance of the proposed controller is tested through simulations.

Seguimiento de trayectoria considerando incertidumbres aditivas en robots móviles

This paper proposes a new control law which allows the tracking trajectory of a mobile robot considering additive uncertainties. A control scheme based on linear algebra is obtained. Furthermore, the optimal controller parameters are chosen by the Monte Carlo experiment. Experimental results are shown and discussed to demonstrate the efficiency of the controller.