Disturbance modeling for offset-free linear model predictive control

Muske, Kenneth R.; Badgwell, Thomas A.

doi:10.1016/s0959-1524(01)00051-8

Cited by 403 publications

(281 citation statements)

References 27 publications

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“…In order to study the tracking performance of the adaptive decoupling switching controller, the product density is changed from 1429.4 to 1435 kg/m 3 at t = 0-2 h. For comparison, the linear GPC control strategy is adopted in this process. The product density is changed from 1435 to 1440 kg/m 3 at t = 2-5 h, and the effect of changes of the specific heat of the liquor are simulated during the process, the specific heat of the liquor is varied from 3.61 to 3.63 kJ/(kg· kJ/(kg·°C) at t = 3 h. The offset-free MPC [28], [29] strategy is adopted for the comparison. Effect of the external disturbance are simulated during t = 5−7 h, where a random bounded disturbance is added to the feed flow rate and the feed density at t = 5, the ranges of the feed flow rate and the feed density are 76-84 m 3 /h and 1361-1369 kg/m 3 , respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

Adaptive Decoupling Switching Control of the Forced-Circulation Evaporation System Using Neural Networks

Wang

Chai

et al. 2013

IEEE Trans. Contr. Syst. Technol.

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Abstract-For the forced-circulation evaporation process of alumina production, the control objectives include maintaining the liquid level and fast tracking of the product density with respect to its setpoint. However, with the strong coupling between the level control and product density control loops and the process exhibits strong nonlinearities, conventional control strategies, such as proportional-integral-derivative and other linear control design, cannot achieve satisfactory control performance and meet production demands. By augmenting the forced-circulation evaporation system model with dynamics of its operating valves, a nonlinear adaptive decoupling switching control strategy is proposed. This control strategy includes a linear adaptive decoupling controller, a neural-network-based nonlinear adaptive decoupling controller, and a switching mechanism. The linear adaptive decoupling controller is used to reduce the coupling between the two control loops. The neural-network-based nonlinear adaptive decoupling controller is employed to improve the transient performance and reduce the effects of the nonlinearities on the system, and the switching mechanism is introduced to guarantee the input-output stability of the closed-loop system. Simulation results show that the proposed method can decouple the loops effectively for the forced-circulation evaporation system and thus improve the evaporation efficiency.

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Section: Simulation Resultsmentioning

confidence: 99%

Adaptive Decoupling Switching Control of the Forced-Circulation Evaporation System Using Neural Networks

Wang

Chai

et al. 2013

IEEE Trans. Contr. Syst. Technol.

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“…In this work, we will keep the same actuators and sensors in all our analysis. states as outputs [18,19]. Based on the augmented system description an ordinary Kalman filter can be designed and an MPC controller synthesised based on the following performance cost function (6) Where h represents the prediction and control horizon, y sp are reference signals for the controlled outputs, y, and the matrices Q and R give relative weights to the contributions from the individual tracking errors and on the speed of the individual actuators respectively.…”

Section: Control Designmentioning

confidence: 99%

Control assessment for heat integrated systems. An industrial case study for ethanol recovery

Mauricio‐Iglesias

Huusom

Sin

2013

Chemical Engineering and Processing: Process Intensification

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Highlights•Heat integration is needed for energy reduction but can deteriorate controllability •Soundly designed controllers can handle complex processes •The deterioration of controllability was handled by the controllers •Large energy savings are possible with a good dynamic response Abstract:Heat integration is essential for reducing the energy consumption of process industries. However, it may render the dynamic operation more interactive and difficult to control. This paper assesses the implications of heat integration in controllability and performance in energy reduction. The assessment, both on open loop and closed loop, was carried out based on an industrial case study and compared to a modified case without heat integration. Although the heat integrated system Page 2 of 31 A c c e p t e d M a n u s c r i p t 2 displayed a certain deterioration of controllability, the control system made possible an efficient operation. To this goal, different control systems were tested, from a decentralized control system to a model predictive controller. The type of controller used in the process barely influenced the performance of the process since the aim was exclusively to ensure the regulation of the process to fixed setpoints. The reduction of energy consumption achieved thanks to heat integration was considerably larger than the losses due to poor control of the process, confirming the importance of heat integration in energy intensive processes.

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“…But it is also possible to use formulations where the disturbance enters the plant in input, output, state or a combination mixed disturbance models. For all of those model types, properties for detectablity are given in (Muske and Badgwellb, 2002). An alternative disturbance modeling scheme is shown in (Pannocchia and Rawlings, 2001) where a mixed model is used for input and output disturbance where the disturbance state is estimated via a standard Kalman filter using such an augmentation of the system and the prediction model (4) it is possible to minimize cost (7) under constraints 2 2 min s.t.…”

Section: Nominal Mpc With (Mixed) Disturbance Model (Md-mpc)mentioning

confidence: 99%

Robust Model Predictive Control of a Diesel Engine Airpath

Langthaler

2008

IFAC Proceedings Volumes

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Model predictive control represents one of the most promising methods, also in fast industrial applications like a Diesel engine. But especially Diesel engine control meets problems of uncertainties and disturbances, thus model-plant mismatch is omnipresent, which obviously decreases the performance of model based control. This paper compares different robust predictive control strategies applied to a Diesel engine airpath.

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Disturbance modeling for offset-free linear model predictive control

Cited by 403 publications

References 27 publications

Adaptive Decoupling Switching Control of the Forced-Circulation Evaporation System Using Neural Networks

Adaptive Decoupling Switching Control of the Forced-Circulation Evaporation System Using Neural Networks

Control assessment for heat integrated systems. An industrial case study for ethanol recovery

Robust Model Predictive Control of a Diesel Engine Airpath

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