Emil Petre

Neural networks-based adaptive control for a class of nonlinear bioprocesses

The paper studies the design and analysis of a neural adaptive control strategy for a class of square nonlinear bioprocesses with incompletely known and time-varying dynamics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed. The neural controller design is achieved by using an input–output feedback linearization technique. The adaptation laws of neural network weights are derived from a Lyapunov stability property of the closed-loop system. The convergence of the system tracking error to zero is guaranteed without the need of network weights convergence. The resulted control method is applied in a depollution control problem in the case of a wastewater treatment bioprocess, belonging to the square nonlinear class, for which kinetic dynamics are strongly nonlinear, time varying and not exactly known.

Adaptive control strategies for a class of anaerobic depollution bioprocesses

This paper presents the design and the analysis of some nonlinear adaptive control strategies for a class of anaerobic depollution processes that are carried out in continuous stirred tank bioreactors. The controller design is based on the input-output linearization technique. The adaptive control structure is based on the nonlinear model of the process and is combined with a state observer and a parameter estimator which play the role of the software sensors for the on-line estimation of biological states and parameter variables of interest of the bioprocess. The resulted control methods are applied in depollution control problem in the case of the anaerobic digestion bioprocess for which dynamical kinetics are strongly nonlinear and not exactly known, and not all the state variables are measurable. The effectiveness and performance of both estimation and control algorithms are illustrated by simulation results.

Neural networks based adaptive control for a class of time varying nonlinear processes

The paper presents the design and analysis of some nonlinear and neural adaptive control strategies for a class of time-varying and nonlinear processes. In fact, a direct adaptive controller based on a radial basis function neural network used as online approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controllers design is achieved by using an input-output feedback linearization technique. Numerical simulations, conducted in the case of a strongly nonlinear, time varying and not exactly known dynamical kinetics fermentation process, are included to illustrate the behaviour and the performance of the presented control laws.

Nonlinear and Neural Networks Based Adaptive Control for a Wastewater Treatment Bioprocess

The paper studies the design and analysis of some nonlinear and neural adaptive control strategies for a wastewater treatment process, which is an activated sludge process with nonlinear, time varying and not exactly known kinetics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed and then is compared with a classical linearizing controller. The neural controller design is achieved by using an input-output feedback linearization technique.

Estimation and adaptive control of a fed-batch bioprocess

This paper deals with estimation and adaptive control strategies for a biotechnological process, which is in fact a lipase production process that takes place inside a fed-batch bioreactor. The lipase production process is highly nonlinear and, furthermore, the available on-line measurements are lack and the reaction kinetics is not perfectly known. On-line state estimation strategies based on extended Luenberger observer and asymptotic observer approach are derived. The unknown kinetic parameters of the bioprocess are estimated by using nonlinear techniques, such as regressive parameter estimator and high-gain observer. The control goal is to maximize the lipase production by controlling the substrate feeding rate. A nonlinear feedback control law is obtained by means of exact linearization technique. By coupling this controller with the parameter estimation algorithms, a nonlinear adaptive controller is obtained. Numerical simulations are included in order to test the behavior and the performance of the proposed estimation and control strategies.

High-gain observers for estimation of kinetics in biological sequencing batch reactors

This paper deals with the problem of on-line estimation of kinetic rates inside biological Sequencing Batch Reactors (SBRs). Two wastewater treatment bioprocesses that are carried out inside SBRs are taken into consideration. These biotechnological processes are highly nonlinear and, furthermore, the available on-line measurements are lack and the reaction kinetics is not perfectly known. The unknown kinetic parameters are estimated by using nonlinear observers, based on high-gain approach. The estimation scheme does not require any model for the kinetic rates. The tuning of the proposed observers is reduced to the calibration of a single parameter. Numerical simulations are included in order to test the behaviour and the performance of the proposed observers.

Bond graph modelling of a wastewater biodegradation bioprocess

This paper addresses the problem of Bond Graph modelling of nonlinear bioprocesses. The rules for the design of pseudo Bond Graph models of some prototype bioprocesses — one batch and one continuous process — are obtained using the reaction schemes and the analysis of biochemical phenomena. These rules are applied in order to design the Bond Graph model of a complex wastewater treatment process, which is a biomethanation process — bio-degradation with production of methane gas. This bioprocess takes place into a Continuous Stirred Tank Bioreactor. The obtained Bond Graph models and several simulations are conducted using 20sim modelling and simulation environment. This modelling procedure represents a valuable illustration of the power of Bond Graph technique, and can be used as a base for the development of the models of bioprocesses with high level of complexity.

An indirect adaptive control strategy for a lactic fermentation bioprocess

This paper presents the design and the analysis of an indirect adaptive control strategy for a lactic acid production that is carried out in two cascaded continuous stirred tank bioreactors. The indirect adaptive control structure is based on the nonlinear process model and is derived by combining a linearizing control law with a new parameter estimator, which plays the role of the software sensor for on-line estimation of the bioprocess unknown kinetics. The behaviour and performance of both estimation and control algorithms are illustrated by simulations applied in the case of a lactic fermentation bioprocess for which kinetic dynamics are strongly nonlinear, time-varying and completely unknown.

Neural Networks Based Adaptive Control of a Fermentation Bioprocess for Lactic Acid Production

This work deals with the design and analysis of some nonlinear and neural adaptive control strategy for a lactic acid production that is carried out in continuous stirred tank bioreactors. An indirect adaptive controller based on a dynamical neural network used as on-line approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controller design is achieved by using an input-output feedback linearization technique. The effectiveness and performance of both control algorithms are illustrated by numerical simulations applied in the case of a lactic fermentation bioprocess for which kinetic dynamics are strongly nonlinear, time varying and completely unknown.

Robust moving horizon state estimation: Application to bioprocesses

In this paper, a robust nonlinear receding-horizon observer is proposed for the estimation of cellular concentration in a bioreactor. In the presence of uncertainties on the model parameter or on the initial state of the system, this estimation problem can lead to poor estimation performance. A min-max optimization solution can be used to increase the robustness of the observer in the presence of parameter uncertainties. This solution assumes that each model parameter belongs to an interval. The paper proposes an alternative modeling for these parameters: A Gaussian model is assumed in order to take into account the correlation between parameters. As the confidence region for the parameters is now an ellipsoid, the max step in the min-max problem is replaced by more tractable statistics. Expected value has been tested for its simplicity. For robustness requirements a statistic considering the variance of the estimation has also been developed. Numerical simulations illustrate the efficiency of the proposed estimation scheme.