Martin Guay

Adaptive extremum seeking control of continuous stirred tank bioreactors with unknown growth kinetics

In this paper, we present an adaptive extremum seeking control scheme for continuous stirred tank bioreactors. We assume limited knowledge of the growth kinetics. An adaptive learning technique is introduced to construct a seeking algorithm that drives the system states to the desired set-points that maximize the value of an objective function. Lyapunovs stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. Simulation results are given to show the effectiveness of the proposed approach.

Adaptive model predictive control for constrained nonlinear systems

In this paper, a method is proposed for the adaptive model predictive control of constrained nonlinear system. Rather than relying on the inherent robustness properties of standard NMPC, the developed technique explicitly account for the transient effect of parametric estimation error by combining a parameter adjustment mechanism with robust MPC algorithms. The parameter estimation routine employed guarantees non-increase of the estimation error vector. This means that the controller employs a process model which approaches that of the true system over time. These estimates are used to update the parameter uncertainty set at every time step, resulting in a gradual reduction in the conservative and/or computational effects of the incorporated robust features.

Finite-Time Parameter Estimation in Adaptive Control of Nonlinear Systems

In most adaptive control algorithms, parameter estimate errors are not guaranteed to converge to zero. This lack of convergence adversely affects the global performance of the algorithms. The effect is more pronounced in control problems where the desired reference set-point or trajectory depends on the systems unknown parameters. In this paper, we present a parameter estimation routine that allows exact reconstruction of the unknown parameters in finite time provided a given excitation condition is satisfied. The algorithm is independent of the control and identifier structure employed. The true parameter value is obtained without requiring the measurement or computation of the velocity state vector. The technique provides a direct solution to the problem of removing auxiliary perturbation signals when parameter convergence is achieved. The effectiveness of the proposed method is illustrated with simulation examples.

Brief paper: Parameter convergence in adaptive extremum-seeking control

This paper addresses the problem of parameter convergence in adaptive extremum-seeking control design. An alternate version of the popular persistence of excitation condition is proposed for a class of nonlinear systems with parametric uncertainties. The condition is translated to an asymptotic sufficient richness condition on the reference set-point. Since the desired optimal set-point is not known a priori in this type of problem, the proposed method includes a technique for generating perturbation signal that satisfies this condition in closed-loop. This demonstrates its superiority in terms of parameter convergence. The method guarantees parameter convergence with minimal but sufficient level of perturbation. The effectiveness of the proposed method is illustrated with a simulation example.

Observer linearization by output-dependent time-scale transformations

We study the problem of observer linearization for single-output dynamical systems in the presence of output-dependent time-scaling changes. An alternative algorithm for the solution of the observer linearization problem is introduced. The algorithm employs an exterior calculus approach that provides a simple procedure for the solution of the observer linearization problem by means of an output dependent time-scale transformation.

Fermentation process development for the production of medium-chain-length poly-3-hyroxyalkanoates

This paper presents a review of the existing fermentation processes for the production of medium-chain-length poly-3-hydroxyalkanoates (MCL-PHAs). These biodegradable polymers are usually produced most efficiently from structurally related carbon sources such as alkanes and alkanoic acids. Unlike alkanoic acids, alkanes exhibit little toxicity but their low aqueous solubility limits their use in high density culture. Alkanoic acids pose little mass transfer difficulty, but their toxicity requires that their concentration be well controlled. Using presently available technology, large-scale production of MCL-PHA from octane has been reported to cost from US

Adaptive extremum-seeking control of a continuous stirred tank bioreactor with Haldane's Kinetics

5 to 10 per kilogram, with expenditures almost evenly divided between carbon source, fermentation process, and the separation process. However, MCL-PHAs, even some with functional groups in their subunits, can also be produced from cheaper unrelated carbon sources, such as glucose. Metabolic engineering and other approaches should also allow increased PHA cellular content to be achieved. These approaches, as well as a better understanding of fermentation kinetics, will likely result in increased productivity and lower production costs.

Original article: Extremum seeking control and its application to process and reaction systems: A survey

In this paper, we present an adaptive extremum seeking control scheme for a continuous stirred tank bioreactor with Haldanes kinetics. The proposed adaptive extremum seeking approach uses the kinetic model of the bioreactor to construct a seeking algorithm that drives the system states to the desired set-points that extremize the value of an objective function. Lyapunovs stability theorem is used in the design of the extremum seeking controller and the development of the parameter updating laws. Simulation experiments are given to show the effectiveness of the proposed approach.

Adaptive extremum seeking control of fed-batch bioreactors

The objective of this paper is to present a survey on extremum seeking control methods and their applications to process and reaction systems. Two important classes of extremum seeking control approaches are considered: perturbation-based and model-based methods.

A Real-Time Framework for Model-Predictive Control of Continuous-Time Nonlinear Systems

In this paper, we present an adaptive extremum seeking controlscheme for fed-batch bioreactors with Haldane kinetics. The proposed adaptive extremum seeking approach utilizes the structure information of the process kinetics to derive a seeking algorithm that drives the system states to the desired set-points that maximize the value of the biomass production. Lyapunovs stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. The performance of the approach is illustrated via numerical simulations.