On-Line Estimation and Adaptive Control of Bioreactors

By G. Bastin, Denis Dochain. 2013

1,483 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8e273eca39529727dfdfd8cd7effd9a07e2382fd

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In Control and Systems Engineering

Citations in the corpus

Stability, dynamics of convergence and tuning of observer-based kinetics estimators (2002, methodology): “The stability conditions were established in [4] and may be summarised as follows:”
Control of a continuous bioreactor using model predictive control (2005, background): “Previous studies reported various types of controller designs including internal model control (IMC) and adaptive controllers, which can lead to stable control [8–12].”
Flat systems, equivalence and trajectory generation (2003, background): “Many simple models of Continuous Stirred Tank Reactors (CSTRs) admit flats outputs with a direct physical interpretation in terms of temperatures or product concentrations [36, 1], as do closely related biochemical processes [4, 17].”
Robust discrete‐time nonlinear sliding mode state estimation of uncertain nonlinear systems (2007, methodology): “1 in [36], we can conclude that the state variables XðtÞ > 0 and SðtÞ > 0 are bounded for all t: Hence Assumption 4 is satisfied.”
Feedback control for nonmonotone competition models in the chemostat (2005, background): “Keywords:Chemostat; Nonmonotone growth functions; Competition model; Feedback control; Competitive dynamical systems; Global stability”
Data-driven Soft Sensors in the process industry (2009, methodology): “The model-driven family of Soft Sensors is most commonly based on First Principle Models (FPM) but model-driven Soft Sensors based on extended Kalman Filter (Welch and Bishop, 2001) or adaptive observer (Bastin and Dochain, 1990) have also been published (e.”
Mathematical modelling of anaerobic digestion of biomass and waste: Power and limitations (2013, background): “As a result, model based controllers are mostly relatively simple, with the most effective ones developed by Bastin and Dochain [66] and Bernard et al.”
Interval State Estimation for a Class of Nonlinear Systems (2012, methodology): “According to [5], the state variable and the control are bounded for all time.”
Dynamic optimization of batch processes: II. Role of measurements in handling uncertainty (2003, background): “(33)), and this also in the presence of uncertainty.”