Constraint handing and stability properties of model‐predictive control

Oliveira, Nuno M.C.; Biegler, Lorenz T.

doi:10.1002/aic.690400706

Cited by 148 publications

(72 citation statements)

References 16 publications

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“…We believe the above formulation is fairly standard for industrial NMPC packages [9,10]. Formulations employing soft output constraints via exact l 1 penalty functions are well-known from literature [11,12] and are used in some industrial NMPC systems [10,13]. The main rationale behind its use is to avoid feasibility problems related to hard output constraints.…”

Section: Nmpc Formulationmentioning

confidence: 99%

Short-term Production Optimization of Offshore Oil and Gas Production Using Nonlinear Model Predictive Control

Willersrud

Imsland

Hauger

et al. 2011

IFAC Proceedings Volumes

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The topic of this paper is the application of nonlinear model predictive control (NMPC) for optimizing control of an offshore oil and gas production facility. Of particular interest is the use of NMPC for direct short-term production optimization, where two methods for (one-layer) production optimization in NMPC are investigated. The first method is the unreachable setpoints method where an unreachable setpoint is used in order to maximize oil production. The ideas from this method are combined with the exact penalty function for soft constraints in a second method, named infeasible soft-constraints. Both methods can be implemented within standard NMPC software tools.The case-study first looks into the use of NMPC for "conventional" pressure control, where disturbance rejection of time-varying disturbances (caused, e.g., by the 'slugging' phenomenon) is an issue. Then the above two methods for production optimization are employed, where both methods find the economically optimal operating point. Two different types of reservoir models are studied, using rate-independent and rate-dependent gas/oil ratios. These models lead to different types of optimums. The relative merits of the two methods for production optimization, and advantages of the two one-layer approaches compared to a two-layer structure, are discussed.

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Section: Nmpc Formulationmentioning

confidence: 99%

Short-term Production Optimization of Offshore Oil and Gas Production Using Nonlinear Model Predictive Control

Willersrud

Imsland

Hauger

et al. 2011

IFAC Proceedings Volumes

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“…To extend the analysis made for the unconstrained case by Santos and Biegler [10] to (11)- (13) we use an exact penalty formulation as developed by Oliveira and Biegler [8]. This approach converts (11)-(13) to the problem P q ðx i Þ:…”

Section: Stability Analysismentioning

confidence: 99%

A tool to analyze robust stability for constrained nonlinear MPC

Santos¹,

Biegler²,

Castro³

2008

Journal of Process Control

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A sufficient condition for robust asymptotic stability of nonlinear constrained model predictive control (MPC) is derived with respect to plant/model mismatch. This work is an extension of a previous study on the unconstrained nonlinear MPC problem, and is based on nonlinear programming sensitivity concepts. It addresses the discrete time state feedback problem with all states measured. A strategy to estimate bounds on the plant/model mismatch is proposed that can be used off-line as a tool to assess the extent of model mismatch that can be tolerated to guarantee robust stability.

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“…Constraint relaxation method [13] was applied to address the feasibility problem due to the temperature constraints. In the case of temperature threshold violations, the temperature constraints in the prediction horizon were relaxed to approach the thresholds asymptotically.…”

Section: B Mpc Controller Implementationmentioning

confidence: 99%

A holistic and optimal approach for data center cooling management

Zhou

Wang

Bash

et al. 2011

Proceedings of the 2011 American Control Conference

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Abstract-Efficient and reliable operation of today's data centers, which host IT equipment with ever-increasing power density, relies heavily on the cooling system to meet the thermal management needs of the IT equipment with minimal environmental footprint. The dynamic IT workload, together with the spatial variance of cooling efficiencies, creates both temporal and spatial non-uniformities within the data centers. Most data centers use zonal cooling actuators, such as computer room air conditioners (CRAC), to alleviate the local "hot spots". Without proper localized cooling actuation mechanisms, the cooling capacity is usually over-provisioned that leads to waste of energy. To address this problem, we introduce adaptive vent tiles (AVT) for local cooling adjustment, and develop a holistic multivariable model based on the mass and energy balance principles to capture the effects of both zonal and local cooling actuation on the inlet temperatures of the racks that host the IT equipment. A model predictive controller is then proposed to minimize the total cooling power while meeting the thermal requirements of the racks. The zonal and local cooling actuation is coordinated in such a unified framework for the provisioning, transport and distribution of the cooling resources in the data centers. The proposed holistic cooling approach is validated in a production data center. Experimental results indicate that up to 36% of CRAC units blower power can be saved, compared with the state of the art control solution.

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Constraint handing and stability properties of model‐predictive control

Abstract: Effects of hard constraints in the stability of model-predictive control (MPC

Cited by 148 publications

References 16 publications

Short-term Production Optimization of Offshore Oil and Gas Production Using Nonlinear Model Predictive Control

Short-term Production Optimization of Offshore Oil and Gas Production Using Nonlinear Model Predictive Control

A tool to analyze robust stability for constrained nonlinear MPC

A holistic and optimal approach for data center cooling management

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