B. Dahhou

Non-linear multivariable adaptive control of an activated sludge wastewater treatment process

In this paper a non-linear adaptive feedback-linearizing control is designed for a biological wastewater treatment model. The adaptive control structure is based on the non-linear model of the process and combined with a joint observer estimator which plays the role of the software sensor for the on-line estimation of biological states and parameter variables of interest of the bioprocess. The performances of both estimation and control algorithms are illustrated by simulation results. They demonstrate effectiveness and significant robustness against measurement noises and kinetic parameter jumps. Copyright

Application of iterative learning control to an exothermic semibatch chemical reactor

Focuses on the temperature control of a semibatch chemical reactor used for fine chemicals production. Such a reactor is equipped with a heating/cooling system composed of different thermal fluids. Without extensive modeling investigations, a feedback-feedforward control strategy is proposed for ensuring the tracking performance of the desired temperature profile. Such a strategy is derived from a family of the iterative learning control (ILC) algorithms named batch model predictive control (BMPC). Learning is achieved without requiring a detailed knowledge of the system, which may be affected by unknown but repetitive disturbances. The learning control solution is based on the minimization of a linear quadratic cost function. The synthesis of the proposed strategy is studied, and improvements of the algorithm features are proposed. First, guaranteed convergence of the algorithm is illustrated in a few experimental runs. Second, some practical considerations for the removal of high-frequency disturbance effects are outlined to improve the achieved performance. Third, a robust supervisory control procedure is employed to choose the right fluid and to reduce the superfluous fluid changeovers, mainly when different fluids are available. Finally, experimental results are presented to illustrate the practical appeal and effectiveness of the proposed scheme.

Adaptive observers based fault detection and isolation for an alcoholic fermentation process

The paper deals with fault detection and isolation in an alcoholic fermentation process. The dynamics involved are nonlinear and the faults are modelled as changes in the system parameters. The fault detection scheme requires combined state and parameter estimation. For this purpose a model reference based estimator is used to develop adaptive observers.

Robust iterative learning control of an exothermic semi-batch chemical reactor

This work focuses on the temperature control of a semi-batch chemical reactor used for flue chemicals production. Such reactor is equipped with a heating/cooling system composed of different thermal fluids. In order to ensure the tracking performance of the desired temperature profile, an iterative learning control (ILC) named batch model predictive control (BMPC) has been adopted. The synthesis of the considered strategy is illustrated and improvements of the algorithm scheme are proposed. Firstly, a guaranteed convergence of the algorithm is illustrated. Secondly, in presence of high frequency disturbance effects, an off-line filtering is adopted for enhancing the achieved performances. Third, a robust supervisory control procedure is employed to choose the right fluid and to reduce the superfluous fluid changeovers, mainly where fluids are of different nature. Finally, the incidence of repetitive disturbances, on line low frequency disturbances and model mismatch are investigated through simulation runs.

Multi-objective optimization of TSK fuzzy models

In this paper we propose a hybrid algorithm to optimize the structure of TSK type fuzzy model using back-propagation (BP) learning algorithm and non-dominated sorting genetic algorithm (NSGA-II). In a first step, BP algorithm is used to optimize the parameters of the model (parameters of membership functions and fuzzy rules). NSGA-II is used in a second phase, to optimize the number of fuzzy rules and to fine tune the parameters. A well known benchmark is used to evaluate performances of the proposed modeling approach, and compare it with other modeling approaches.

Estimation and optimal control design of a biological wastewater treatment process

This paper deals with the on-line estimation and optimal control of a biological wastewater treatment process. The objective of the control is to force the residual substrate and the dissolved oxygen concentrations to track a given reference model despite the disturbances and system parameter uncertainties. The control law is based on one step ahead prediction of the controlled variables and minimization of an appropriate quadratic cost function. The technique is based on direct exploitation of the nonlinear model representing the wastewater treatment process and is coupled with an asymptotic estimator for on-line tracking of simultaneously unavailable states and time varying parameters. The estimated variables are used in the explicit design of the control algorithm according to certainty equivalence principle. A simulation study subject to measurement noise and abrupt jumps in the kinetic parameters shows the feasibility and robustness of the control strategy.

Adaptive control of a water supply system

Abstract This paper deals with the application of discrete-time adaptive control to a freshwater supply system. The main control objective is to regulate the consumption of water-flow by controlling the water pumps discharge. The adaptive control implemented is based on the linear quadratic control approach. A single input/output model is used for the control purposes. The model parameters are estimated on-line using a robust recursive least-squares (RLS) identification algorithm. Experimental results show the performance of this adaptive scheme and its ability to control the water distribution process.

Estimation and filtering of nonlinear systems: application to a wastewater treatment process

A fundamental task in design and control of biotechnological processes is system modelling. This task is made difficult by the scarceness of online direct sensors for some key variables and by the fact that identifiability of models, including the Michaelis-Menten type of nonlinearities, is not straightforward. The use of adaptive estimation approaches constitutes an interesting alternative to circumvent these kinds of problems. This paper discusses an identification technique derived to solve the problem of estimating simultaneoulsy inaccessible state variables and time-varying parameters of a nonlinear wastewater treatment process. An extended linearization technique using Kroneckers calculation provides the error model of the joint observer-estimator procedure, whose convergence is proved via Lyapunovs method. Sufficient conditions for stability of this joint identification scheme are given and discussed according to the persistency excitation conditions of the signals. A simulation study with measur...

Unstructured modelling growth of Lactobacillus acidophilus as a function of the temperature

We present modelling software developed under MATLAB in which parameter estimations are obtained by using non-linear regression techniques. The different parameters appear in a set of non-linear algebraic and differential equations representing the model of the process. From experimental data obtained in discontinuous cultures a representative mathematical model (unstructured kinetic model) of the macroscopic behaviour of Lactobacillus acidophilus has been developed. An unstructured model expressed the specific rates of cell growth, lactic acid production and glucose consumption for batch fermentation. The model is formulated by considering the inhibition of growth under sub-optimal culture conditions during Lactobacillus acidophilus fermentation, which is accompanied by an increase of the maintenance energy. This study permits to predict the cellular behaviour at low growth temperatures and enables to define the response of the strain to sub-optimal temperature stress.

Modelling and estimation aspects of adaptive predictive control in a fermentation process

Abstract This paper describes some engineering aspects of the design of high-performance control systems. The importance of accurate modelling of relevant process dynamics is outlined. The design of a model structure, the identification of the model parameters and the controller must then be seen as three parts of a joint design problem. The long-range predictive control problem is addressed in the context of linear modelling, static nonlinearity combined with linear modelling, and nonlinear modelling, where the parameters are determined by using either a standard single-step-ahead estimator or a multi-step-ahead estimator. An experimental evaluation is performed on a continuous stirred-tank fermentation process which exhibits nonlinear and unstationary features.