Michael Kawohl

Robust model predictive control using the unscented transformation

This paper presents a new approach for robust open-loop and closed-loop control of nonlinear processes with parameter uncertainties and a comparison with classical concepts. The approach leads to trajectories that show small variations if uncertain parameters and uncertain initial conditions are present. The algorithm utilizes the Unscented Transformation. It allows a 2nd order approximation of the first two statistical moments of the systems output as a function of the stochastic systems state and uncertain model parameters. Because the numerical burden is low, it can be used for optimization based online closed-loop process control as well

Rechnergestützte halbautomatische Modellierung biotechnologischer Prozesse (Semiautomatic Modeling of Biotechnical Processes)

In diesem Beitrag wird ein Software-Werkzeug vorgestellt, das es ermöglicht, mathematische Modelle für biologische Reaktionssysteme weitgehend automatisch und in effizienter Weise zu erstellen oder zu verifizieren. Das Werkzeug basiert auf einer Kombination von statistischen, regelbasierten und qualitativen Methoden, mit denen wesentliche Informationen bezüglich möglicher Modellstrukturen gewonnen und postulierte Modellansätze systematisch überprüft werden können. Automatisierte quantitative Verfahren werden dann nur auf eine kleine Untermenge qualitativ korrekter Modelle angewandt, um letztendlich ein Modell vorzuschlagen. This paper presents a software-tool which makes it possible to generate or to verify mathematical models for biological reaction systems automatically in an efficient manner. The tool is based on a combination of statistical, rule-based and qualitative methods to obtain information concerning possible model structures and for testing postulated model candidates, systematically. Only those models are finally dealt with quantitatively, which have passed the qualitative check.

EXPERIMENTAL COMPARISON OF MODEL PREDICTIVE CONTROL STRATEGIES FOR THE PRODUCTION OF ANTIBIOTICS IN FED-BATCH FERMENTATIONS

Abstract The main objective of the presented work is to compare two model predictive control strategies by applying them to an antibiotic production in a fed-batch fermentation process. The reactor is modelled as a nonlinear biological compartment system with 13 states. The well-known nonlinear model predictive control (NMPC) is compared with a control strategy based on online optimization of the full trajectory. The control strategies are applied to real fermentation processes, which are strongly disturbed by a temperature shift. To extend the comparison, two state estimators, the Extended-Kalman-Filter (EKF) and the Constrained-EKF (CEKF), are used.

Ein neuer Ansatz zur robusten Trajektorienplanung und robusten nichtlinearen modellprädiktiven Regelung für Prozesse mit Parameterunsicherheiten (A New Approach for Robust Optimization Based Open- and Closed-Loop Control of Nonlinear Processes)

Der Beitrag stellt einen neuen Ansatz zur robusten Prozessführung vor und vergleicht ihn mit bekannten Ansätzen. Das vorgeschlagene Verfahren basiert auf der Unscented Transformation. Es ermöglicht mit vergleichsweise geringem numerischen Aufwand selbst im on-line-Betrieb die Berechnung der statistischen Momente des Systemausgangs als Funktion der stochastisch verteilten Zustandsgrößen und der zufälligen Modellparameter. Diese Information kann gewinnbringend in der Optimierung verwendet werden. So können trotz Unsicherheiten z.B. sicherheitskritische Grenzen eines Prozesses mit hoher Wahrscheinlichkeit eingehalten werden. This paper presents a new approach for robust open-loop and closed-loop control of nonlinear processes with parameter uncertainties and a comparision with classical approaches. The algorithm uses the Unscented Transformation. It allows the calculation of statistic moments of the systems output as a function of stochastic system states and model parameters. Because the numerical burden is low, it can be used for optimization based online closed-loop process control as well.

Realisierung von Advanced Control in einem industriellen Polymerisationsprozess

Zusammenfassung Der vorliegende Beitrag zeigt exemplarisch an einem industriellen Produktionsprozess das Potenzial modellbasierter Regelungsverfahren auf. Ziel der Optimierung ist die Verbesserung der Produktqualität durch eine präzisere Temperaturregelung und die Reduzierung der Batchzeiten. Hierfür wird ein nichtlinear modellbasierter prädiktiver Regler, welcher die Kühlleistung und die Monomerdosierung als Stellgrößen nutzt, implementiert. In der Produktion kann die Regelgüte der Temperaturregelung wesentlich verbsssert und die Batchzeit erheblich reduziert werden.

Robust Trajectory Planning and Robust NMPC of Processes with Model Uncertainties

Abstract This paper presents an approach for robust open-loop and robust closed-loop control of biological production processes described by models which comprise uncertain initial conditions and parameter uncertainties. The application of these robust techniques dramatically decreases the variability of the realized process trajectory and hence, the fluctuation of the product amount. The algorithm utilizes a second order approximation to calculate the first two statistical moments of the systems output as a function of the stochastic systems state and uncertain model parameters. Extensive simulation studies of both open- and closed-loop controlled fermentations based on a simple unstructured production model as well as on a structured compartment model consisting of 7 biotic states are shown.

A Comparison of Three Approaches for Control of Fed-Batch Process for Antibiotic Production by Streptomyces Strains

Abstract The main objective of the work is to investigate three different algorithms for control of antibiotic production during a fed-batch fermentation of Streptomyces strain and to compare the results from a user’s point of view. The reactor is modeled as a nonlinear biological compartment system with 13 states and 51 parameters. A control strategy based on online optimization of the full trajectory is compared separately with both other strategies (algorithms). The first one is a linearizing adaptive control and the second is an adaptive control designed by using the MATLAB toolbox DESIGNER. The main control task is to obtain maximum antibiotic concentration in the reactor controlling feed rates of three limiting substrates (or the most important among them) maintaining of all state restrictions. The verification of each control algorithm is realized by computer simulation of a control system that includes the structured process (reactor) model. The advantages and disadvantages of the investigated approaches are discussed.