Raymond Hanus

Conditioning technique, a general anti-windup and bumpless transfer method

Abstract This paper gives a general way to take into account, by an appropriate design of the controller, any discrepancy which can occur between the actual inputs of a process and the desired outputs of its controller. This yields to the so-called conditioned control algorithms. The conditioning technique is described for multiple-input-multiple-output nonlinear controllers. Application to complex control structures is explained. The notion of a self-conditioned controller is defined in order to simplify the implementation of the conditioning technique. Some considerations about the stability of conditioned systems are given. The automatic initialization of any control algorithm is given as an application of the method. It shows that bumpless transfer can be achieved.

Anti-windup, bumpless, and conditioned transfer techniques for PID controllers

Gives a simple and comprehensive review of anti-windup, bumpless and conditioned transfer techniques in the framework of the PID controller. We show that the most suitable anti-windup strategy for usual applications is the conditioning technique, using the notion of the realizable reference. The exception is the case in which the input limitations are too restrictive. In this case, we propose the anti-windup method with a free parameter tuned to obtain a compromise between the incremental algorithm and the conditioning technique. We also introduce the new notion of conditioned transfer, and we it to be a more suitable solution than bumpless transfer. All the discussions are supported by simulations.

Conditioning technique for controllers with time delays

The so-called conditioning technique has been shown to be a powerful antiwindup and bumpless transfer method. It gives a way to take into account, by appropriate design of the controller, any discrepancy which can occur between the actual control variables and the desired control variables. However, the original version of this method is restricted to the case where the controller has no time delay. The authors propose a modified version of the conditioning technique that can handle the case where the controller has a general time-delay structure. >

On-line state estimation of bioprocesses with full horizon observers

Software sensors (or state observers) are able to provide a continuous estimation of some signals (e.g. concentrations of important culture components, like biomass) which are not measured by hardware sensors. They need a mathematical model of the process and (discrete) hardware measurements of some other signals, like the concentrations of the main substrates. In this contribution, the state observer (called full horizon observer) is based on the identification of the most likely initial conditions of the experiment, e.g. the initial concentrations of the culture, these latter being identified at each time where new measurements are available. The basic principles of this observer are given in the general framework of nonlinear systems. Some properties and extensions of this state estimation method are presented. Some comparisons with the linear and extended Kalman filters are also given. The observer performances are illustrated in the case of the biomass concentration estimation within CHO animal cell cultures, for which only rare and asynchronous measurement samples of the glutamine, glucose and lactate concentrations are available.

A new decoupling precompensator for indirect adaptive control of multivariable linear systems

This note presents an indirect adaptive control algorithm for a class of multiple-input multiple-output (MIMO) linear systems. The controller consists of four parts. A precompensator and a feedback filter diagonalize the transfer function between a set of auxiliary control variables v i and a set of auxiliary output variables x i . Each SISO system with input v i and output x i is then easily controlled by standard SISO pole placement techniques. A model of the system is identified on-line, and is used to update the parameters of the precompensator, the feedback filter, and the SISO controllers. The main advantage of such an algorithm lies in the decoupling of the closed-loop system. This quality is fundamental for tracking performances. The a priori knowledge required for implementation of the algorithm consists of the system observability indexes.

Incorporating batch effects in the estimation of drug stability parameters using an Arrhenius model

The nonlinear estimation of drug stability parameters (energy of activation Ea and shelf-life tY) by conventional approaches employs equations relating drug content determination C at time t and temperature T. The identification procedures lead to the determination of only one initial drug content C0 for several different experiments. However, it is well known that because of experimental concentration variation or of intentional modification of the experimental schedule, there are as many initial drug contents as experiments. For these reasons, a method which takes into account batch effects is proposed to determine stability parameters and also all initial drug contents C0j where j is the index of experiment in one step. This method is more accurate from a statistical viewpoint and is suitable for data treatment in pharmaceutical industries where the initial drug content of each batch entering the stability program can be checked a posteriori. The application of this method is shown on real kinetic data from the hydrolysis of acetylsalicylic acid (ASA).

Macroscopic Modelling of Bioprocesses with a View to Engineering Applications

Several motivations exist to use macroscopic models for engineering applications and to define a general modelling methodology. In this context, the framework of system of mass balances based on macroscopic reaction schemes is recalled and a new general kinetic model structure is presented and analysed. A general methodology for the parameter identification (kinetic and pseudo-stoichiometric coefficients) is summarised. Necessary conditions of validation of the reaction scheme (based on the identified model parameters) are proposed. The flexibility of the general kinetic model structure and a part of the parameter identification methodology are illustrated on simulated bacteria cultures.

Maximum likelihood estimation of pseudo-stoichiometry in macroscopic biological reaction schemes

Identification of pseudo-stoichiometric (or yield) coefficients is of primary importance for building a bioprocess model. In most of the applications, the estimation of these coefficients has to be performed without any knowledge of the kinetics and on the basis of a few experiments for which noisy discrete measurements of component concentrations are available. This paper proposes maximum likelihood estimators which are able to deal with measurement errors on all the signals, at each sampling time (including the initial one) and with intrinsic sign constraints on the parameters. This kind of realistic hypotheses exclude the use of the usual (weighted) least-squares estimators. The maximum likelihood estimators are proved to be unbiased (provided a first-order approximation) and their estimation error covariance matrix can be computed (at the same level of first-order approximation). The solutions are proposed in a very general framework, dealing with cell cultures (of bacteria, yeasts or animal cells) performed in stirred tank (continuous, semi-batch or batch) reactors, and without any a priori knowledge on the kinetics. The use of the estimators and their statistical properties are illustrated in a simulation case study (fed-batch bacterial cultures) and in a real case one (batch animal cell cultures).

Improved kinetic parameter estimation in pH-profile data treatment

Statistical problems in temperature stability parameter estimation have been the subject of many papers whereas statistics in, pH-profile parameter estimation have focused little attention. However, the conventional two step method used in data treatment in both cases leads to identical statistical problems. The aim of this study is then to introduce a method that improves statistics in pH-profile parameter estimation. A one step non-linear method that takes into account the errors in drug content determination is proposed. A mathematical relationship between drug content C, pH and time t is tested. The proposed method allows the estimation of the specific kinetic constants and the dissociation constant (pK(a)) in a single run. The most likely experimental initial drug contents C(0j),. where j is the index of a given experiment, are also determined. This approach that takes into account all relevant experimental information for the estimation of kinetic parameters is more rigorous from a statistical viewpoint than the classical two step methods. Kinetic data from acetylsalicylic acid (ASA) hydrolysis was used for the tests.

Parametric uncertainties and influence of the dead volume representation in modelling simulated moving bed separation processes

In this study, a systematic numerical procedure for identifying the model parameters of simulated moving bed (SMB) separation processes is developed. The parameters are first estimated by minimizing a weighted least-squares criterion using experimental data from batch experiments, e.g. the time evolution of the concentration of elution peaks. Then, a cross-validation is achieved using data from experiments in SMB operation. At this stage, the importance of a careful modelling of the dead volumes within the SMB process is highlighted. In addition, confidence intervals on the estimated parameters and on the predicted concentration profiles are evaluated.