Application of state estimation based NMPC to an unstable nonlinear process

Biagiola, Silvina Ines; Figueroa, José Luis

doi:10.1016/j.ces.2004.07.026

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…Forssell and Ljung [1998]). In nonlinear MPC, instability is often tackled in terms of control of the process as close as possible to an open-loop unstable steady state (which is of production interest), like in (Biagiola and Figueroa [2004]). In Heidarinejad et al [2013], a Lyapunov based approach is used in an economic MPC with a two-mode operation controller that helps to stabilize the system state in closed loop in a prescribed relatively large region.…”

Section: Mpc Techniquesmentioning

confidence: 99%

Observer and model predictive control for on-line parameter identification in nonlinear systems

Qian

Nadri

2013

IFAC Proceedings Volumes

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International audienceThis paper develops an on-line model parameter identification approach for mul- tivariable systems, which are nonlinear in terms of state representation and/or in terms of parameters. Combining the observation theory and the model based predictive control theory, an optimal closed loop experiment design for on-line identification of model parameters is given. During only one experiment, an optimal time-varying input is computed to optimize a criterion, while the unknown model parameters are estimated at the same time. The criterion is based on the sensitivities of the model outputs with respect to the unknown parameters that are estimated. The approach does not require to measure all the process state. Moreover output constraints allow to maintain the behaviour into a prescribed region and/or stabilize the process in closed loop. This approach is illustrated through an unstable rolling delta wing with one input, two measured states and five unknown constant model parameter

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Section: Mpc Techniquesmentioning

confidence: 99%

Observer and model predictive control for on-line parameter identification in nonlinear systems

Qian

Nadri

2013

IFAC Proceedings Volumes

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“…When high nonlinearities are included in the mathematical model of the process, the extended version of the Kalman observer should be used (Biagiola and Figueroa, 2004).…”

Section: Extended Kalman Observer (Eko)mentioning

confidence: 99%

Design of on-Line State Estimators for a Recombinant E. Coli Fed-Batch Fermentation

Veloso

Rocha

Ferreira

2005

IFAC Proceedings Volumes

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In recent years a remarkable effort has been made in the development of new sensors and process analytical technology. However, it is still difficult to find reliable and low cost commercial sensors for on-line measurements of important variables. Therefore, considerable attention has been focused on the development of on-line software sensors. Nevertheless, the application of those algorithms to complex biological processes is still very incipient. In this work two different state estimators have been studied regarding their applicability to the recombinant Escherichia coli fed-batch fermentation. Both algorithms showed the ability to estimate on-line biomass and acetate concentrations. However, the extended Kalman observer exhibited a better convergence in spite of being less flexible regarding the combination of the measured and estimated variables.

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“…Both the unmeasured disturbances and the states are estimated (Biagiola and Figueroa, 2004a;2004b). A nonlinear controller (Biagiola and Figueroa, 2004a) based on exact linearization is designed.…”

Section: Introductionmentioning

confidence: 99%

“…A nonlinear controller (Biagiola and Figueroa, 2004a) based on exact linearization is designed. A nonlinear model predictive controller (Biagiola and Figueroa, 2004b) is introduced. An adaptive extremum-seeking controller based on a reduced-order observer is designed for optimization of the production rate (Marcos et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Output feedback finite-time control for a bioreactor system based on a finite-time stable observer

Zhao

Fei

2012

Transactions of the Institute of Measurement and Control

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In this paper, an output feedback finite-time controller based on a finite-time stable observer is developed for a bioreactor system. The observer is proposed to estimate the biomass concentration in finite time. Based on the estimation of the biomass concentration, an output feedback controller is designed to track the reference signal in finite time. A Lyapunov function is constructed to prove that the observer is a finite-time stable observer. The finite-time stability results for cascaded systems are employed to prove that the closed-loop system satisfies the finite-time stability. Thus, the observer and the controller can be designed separately, both with finite-time convergence. Numerical simulation results demonstrate the effectiveness of the method.

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Application of state estimation based NMPC to an unstable nonlinear process

Cited by 14 publications

References 21 publications

Observer and model predictive control for on-line parameter identification in nonlinear systems

Observer and model predictive control for on-line parameter identification in nonlinear systems

Design of on-Line State Estimators for a Recombinant E. Coli Fed-Batch Fermentation

Output feedback finite-time control for a bioreactor system based on a finite-time stable observer

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