Modelling and Adaptive Control of Aerobic Continuous Stirred Tank Reactors

Georgieva, Pétia; Ilchmann, Achim; Weirig, Marie-France

doi:10.3166/ejc.7.476-491

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…Theoretical results and simulations for mostly biotechnological applications of λ-tracking are as follows: in anesthesia in [3]; for continuous stirred tank reactors in [1,6]; for exothermic chemical reactors under input constraints in [24]; for chemical reaction models with sampleddata in [25]; for activated sludge processes in [14].…”

Section: Applicationsmentioning

confidence: 99%

High‐gain control without identification: a survey

Ilchmann¹,

Ryan

2008

GAMM-Mitteilungen

111

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Three related but distinct scenarios for tracking control of uncertain systems are reviewed: asymptotic tracking, approximate tracking with prescribed asymptotic error bound, tracking with prescribed transient behaviour. A variety of system classes are considered, ranging from finite-dimensional linear minimum-phase systems to nonlinear, infinite-dimensional systems described by functional differential equations. These classes are determined only by structural assumptions, such as stable zero dynamics and known relative degree. The objective is a single (and simple) control structure which is effective for every member of the underlying system class: no attempt is made to identify the particular system being controlled.

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Section: Applicationsmentioning

confidence: 99%

High‐gain control without identification: a survey

Ilchmann¹,

Ryan

2008

GAMM-Mitteilungen

111

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“…The topology of the identification RTNN is (1, 3, 3, 1) and the control RTNN has a topology (1,3,3,1). Note that both networks contain two hidden layers (one recurrent and one feedforward) and one feedforward output layer.…”

Section: Inverse Model Adaptive Rtnn Controlmentioning

confidence: 99%

“…The network topology is (1,3,1); that is, it has one hidden layer with three neurones. RTNN is provided on-line by the measured process input, which serves as the network input, and the measured process output, which serves as the network target.…”

Section: Rtnn Process State Estimationmentioning

confidence: 99%

“…The identification RTNN has a topology (1,3,1) and the control parameter is set to g ϭ 0.1. The results show a good convergence of the system output to the desired trajectory after approximately 2.1 h. The graphics of the learned matrix parameters A, B, and C are omitted here because they are constant due to the small MSE (in percent), which is below 2%.…”

Section: Indirect Adaptive Rtnn Controlmentioning

confidence: 99%

“…This problem is avoided in Ref. 3, where adaptive Lambda-tracking control is implemented for activated sludge processes. The Lambda tracker is simple in its design, relies only on structural properties of the process and weak feasibility conditions, and acts as a proportional controller with adaptive error-dependent gain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive recurrent neural network control of biological wastewater treatment

Baruch

Georgieva

Barrera-Cortés

et al. 2004

Int. J. Intell. Syst.

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Three adaptive neural network control structures to regulate a biological wastewater treatment process are introduced: indirect, inverse model, and direct adaptive neural control. The objective is to keep the concentration of the recycled biomass proportional to the influent flow rate in the presence of periodically acting disturbances, process parameter variations, and measurement noise. This is achieved by the so-called Jordan Canonical Recurrent Trainable Neural Network, which is a completely parallel and parametric neural structure, permitting the use of the obtained parameters, during the learning phase, directly for control system design. Comparative simulation results confirmed the applicability of the proposed control schemes.

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Estimation of kinetic rates in a baker's yeast fed-batch bioprocess by using non-linear observers

Selişteanu

Petre

Roman

et al. 2012

IET Control Theory Appl.

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The growth rates of the microorganisms in bioreactors are described by complex kinetic expressions, and the modelling and estimation of such kinetics are essential for the design of control strategies. This study deals with the online estimation of kinetic rates of a baker's yeast process, taking place inside a fed-batch bioreactor. This bioprocess is widely used in bioindustry; its model is highly non-linear and, furthermore, the available online measurements are missing and the reaction kinetics is not perfectly known. The unknown kinetics is estimated by using non-linear observers, based on high-gain approach. Two high-gain observers are designed and implemented: one for the specific growth rates and the other for the reaction rates of the baker's yeast bioprocess. The multiple estimation schemes do not require any model for the kinetic rates. The tuning of the proposed observers is reduced to the calibration of a single parameter. An observer-based estimator is also implemented in order to use it for comparisons. Numerical simulations are included to test the behaviour and the performance of the proposed observers.

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Modelling and Adaptive Control of Aerobic Continuous Stirred Tank Reactors

Cited by 8 publications

References 11 publications

High‐gain control without identification: a survey

High‐gain control without identification: a survey

Adaptive recurrent neural network control of biological wastewater treatment

Estimation of kinetic rates in a baker's yeast fed-batch bioprocess by using non-linear observers

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