Nonlinear model‐predictive control: Closed‐loop stability analysis

Sistu, Phani B.; Bequette, B. Wayne

doi:10.1002/aic.690421210

Cited by 36 publications

(20 citation statements)

References 37 publications

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“…Simulink (MATHWORKS, Inc., Natick, MA) provides the simulated environment for this work. Stability of GPC is usually ensured by proper choices of prediction, control horizons, and weighting matrixes in the objective function (26). The GPC problem solved in this work also constrains the oxygen mole fraction into the sparger between 0.0 and 0.209.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Identification and Control of Dissolved Oxygen in Hybridoma Cell Culture in a Shear Sensitive Environment

Simon

Karim

2001

Biotechnol. Prog.

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The productivity of mammalian cells can be enhanced by facilitating adequate oxygen transfer into the cultivation medium. However, current methods of controlling dissolved oxygen (DO) fail to account for alterations in medium composition during the course of the fermentation. These changes, which directly affect gas solubility and overall mass transfer coefficient, may be significant and deteriorate controller's performance in the long run. In this paper, the applications of Generalized Predictive Controllers (GPC) to DO control were investigated in a shear sensitive environment and compared to PID and Model Predictive Controllers (MPC). Input and output data for system identification were initially generated by varying the composition of oxygen fed into the bioreactor from 0 to 0.21 mol % while keeping the total inlet gas flow rate at 8.75 vvm. The process was identified using an AutoRegressive model with eXogeneous inputs (ARX) model and tested on different data sets. The model parameters were then correlated with the overall mass transfer coefficients. In simulation tests, the output of the PID controller switched from minimum to maximum values while more continuous control signals were obtained with the MPC and GPC controllers. When tested in a cell-free medium, all three controllers were able to track setpoint changes with some chattering observed in the control signals. The GPC outperformed the MPC and PID controllers when applied to the cultivation of hybridoma cells.

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Section: Experiments and Resultsmentioning

confidence: 99%

Identification and Control of Dissolved Oxygen in Hybridoma Cell Culture in a Shear Sensitive Environment

Simon

Karim

2001

Biotechnol. Prog.

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“…Closed loop stability analysis has now since long been studied with MPC for non-linear systems (e.g. [13,16]). For domestic energy management of heat energy this method has been successfully applied in both in open and closed loop instances [4,6,11].…”

Section: Previous Work Related To Adaptive Mpcmentioning

confidence: 99%

Adaptive heat pump and battery storage demand side energy management

et al. 2017

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Abstract. An adaptive linear model predictive control strategy is introduced for the problem of demand side energy management, involving a photovoltaic device, a battery, and a heat pump. Moreover, the heating influence of solar radiation via the glass house effect is considered. Global sunlight radiation intensity and the outside temperature are updated by weather forecast data. The identification is carried out after adapting to a time frame witch sufficiently homogeneous weather. In this way, in spite of the linearity an increase in precision and cost reduction of up to 46% is achieved. It is validated for an open and closed loop version of the MPC problem using real data of the ambient temperature and the global radiation.

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“…2) Synthesis for Restricted Plant Structure: Given this receding horizon framework and the notion of an influence vector, we now restrict attention to a set of fixed conditions and design a satisficing controller for the resulting structure. The use of an influence vector allows us to account for the relative order of the system (see, for example [4]) while using Euler integration. We can thus use this simple discretization method (the first restriction) producing the discrete-time dynamical expression given by (25) where is the sampling time.…”

Section: Applications Of Satisficing Controlmentioning

confidence: 99%

A theory of satisficing decisions and control

Goodrich

Stirling

Frost

1998

IEEE Trans. Syst., Man, Cybern. A

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The existence of an optimal control policy and the techniques for finding it are grounded fundamentally in a global perspective. These techniques can be of limited value when the global behavior of the system is difficult to characterize, as it may be when the system is nonlinear, when the input is constrained, or when only partial information is available regarding system dynamics or the environment. Satisficing control theory is an alternative approach that is compatible with the limited rationality associated with such systems. This theory is extended by the introduction of the notion of strong satisficing to provide a systematic procedure for the design of satisficing controls. The power of the satisficing approach is illustrated by applications to representative control problems.

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Nonlinear model‐predictive control: Closed‐loop stability analysis

Cited by 36 publications

References 37 publications

Identification and Control of Dissolved Oxygen in Hybridoma Cell Culture in a Shear Sensitive Environment

Identification and Control of Dissolved Oxygen in Hybridoma Cell Culture in a Shear Sensitive Environment

Adaptive heat pump and battery storage demand side energy management

A theory of satisficing decisions and control

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