Adaptive identification and control algorithms for nonlinear bacterial growth systems

DOI10.1016/0005-1098(84)90012-8zbMath0544.93043OpenAlexW2013597661MaRDI QIDQ797545

Georges Bastin, Denis Dochain

Publication date: 1984

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0005-1098(84)90012-8

zbMATH Keywords

nonlinear adaptive controladaptive minimum variance control algorithmbacterial growth systems

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Adaptive control/observation systems (93C40) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12) Physiological, cellular and medical topics (92Cxx)

Related Items

On-line estimation of microbial specific growth rates ⋮ Discretization error bounds for sampled-data control of nonlinear systems ⋮ Sampled-data state feedback stabilization of a class of nonlinear systems based on Euler approximation ⋮ The control of fed-batch fermentation processes. A survey ⋮ Nonlinear identification and control of a heat exchanger: A neural network approach ⋮ Integral versions of input-to-state stability for dual-rate nonlinear sampled-data systems ⋮ Design of an adaptive bilinear power system stabilizer ⋮ Design of adaptive linearizing controllers for non-isothermal reactors ⋮ Efficient output solution for nonlinear stochastic optimal control problem with model-reality differences ⋮ Simple nonlinear observers for on-line estimation of kinetic rates in bioreactors ⋮ Self-tuning control of non-linear ARMAX models ⋮ Changing supply rates for input-output to state stable discrete-time nonlinear systems with applications ⋮ Adaptive predictive control of a multistage fermentation process ⋮ Modelling and adaptive control of nonlinear distributed parameter bioreactors via orthogonal collocation ⋮ A new adaptive PI for bicarbonate regulation in an anaerobic digestion process ⋮ Modelling and adaptive predictive control of a continuous fermentation process ⋮ Sufficient conditions for stabilization of sampled-data nonlinear systems via discrete-time approximations ⋮ Self-tuning controllers for nonlinear systems ⋮ Global dynamics of the buffered chemostat for a general class of response functions

Cites Work

• State and parameter estimation of microbial growth processes
• Procedures for parameter and state estimation of microbial growth process models
• Adaptive control and identification of the dissolved oxygen process
• On type 1 systems and the Clarke-Gawthrop regulator
• Unnamed Item
• Unnamed Item
• Unnamed Item