Model predictive control with linear models

Muske, Kenneth R.; Rawlings, James B.

doi:10.1002/aic.690390208

Cited by 589 publications

(322 citation statements)

References 32 publications

(33 reference statements)

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“…Consequently, the state estimate may not be accurate enough in the presence of model perturbation. We therefore follow common practice in the literature [Astrid et al, 2002, Muske andRawlings, 1993], and use a Kalman filter, which is known to have desirable properties for systems with noise in outputs and state equations. The Kalman filter is here defined in terms of the discretized reduced model with added noise,…”

Section: Reduced-order Mpcmentioning

confidence: 99%

Complexity Reduction in Explicit MPC through Model Reduction

Hovland

Gravdahl

2008

IFAC Proceedings Volumes

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In this paper we propose to use model reduction techniques to make explicit model predictive control possible and more attractive for a larger number of applications and for longer control horizons. The main drawback of explicit model predictive control is the large increase in controller complexity as the problem size increases. For this reason, the procedure is limited to applications with low-order models, a small number of constraints and/or short control horizons. The proposed use of model reduction techniques is demonstrated for several applications, among others for control of fuel cell breathing. In all applications, a significant reduction in controller complexity is achieved.

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Section: Reduced-order Mpcmentioning

confidence: 99%

Complexity Reduction in Explicit MPC through Model Reduction

Hovland

Gravdahl

2008

IFAC Proceedings Volumes

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“…This approach has been analyzed for the infinite horizon case in, e.g., Muske and Rawlings (1993) and Rawlings et al (1994). For nonlinear systems simulation results are presented in Meadows and Badgwell (1998), however without any detailed stability analysis.…”

Section: Reference Trackingmentioning

confidence: 99%

“…At this steady state it shall hold that y k = r, where r is the external reference to be followed. A reasonable choice of steady state is the minimal norm input, which can be formulated as a convex problem, (Muske and Rawlings, 1993) min.…”

Section: Reference Trackingmentioning

confidence: 99%

Reference Tracking MPC Using Dynamic Terminal Set Transformation

Simon

Löfberg

Glad

2014

IEEE Trans. Automat. Contr.

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Among the many different formulations of Model Predictive Control (MPC) with guaranteed stability, one that has attracted significant attention is the formulation with a terminal cost and terminal constraint set, the so called dual mode formulation.In this paper our goal is to make minimal changes to the dual mode framework, for the linear polytopic case, in order to develop a flexible reference tracking algorithm with guaranteed stability and low complexity, which is intuitive and easily understood. The main idea is to introduce a scaling variable that dynamically scales the terminal constraint set and therefore allows it to be centered around an arbitrary setpoint without violating the stability conditions. The main benefit of the algorithm is reduced complexity of the resulting QP compared to other state of art methods without loosing performance.

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“…As discussed in the previous section, the plant is stable, therefore, according to [14], Q N is defined as the infinite sum:…”

Section: Constrained Receding Horizon Regulationmentioning

confidence: 99%

Model Predictive Control of Thermal Comfort and Indoor Air Quality in livestock stable

Rajamani

Rawlings

et al. 2007

2007 European Control Conference (ECC)

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Abstract-In this paper, the implementation of a Model Predictive Control (MPC) strategy for livestock ventilation systems and the associated indoor climate through variable valve opening and exhaust fan, is presented. The design is based on Thermal Comfort (TC) and Indoor Air Quality (IAQ) parameters for poultry in barns. The dynamic models describing the nonlinear behavior of ventilation and associated indoor climate, by applying a so-called conceptual multi-zone method are used for prediction of indoor horizontal variation of temperature and carbon dioxide concentration. The simulation results illustrate the significant potential of MPC in dealing with nonlinearities, handling constraints and performing offset free tracking for multiple control objectives. The entire control systems are able to determine the demand ventilation rate and airflow pattern, optimize the Thermal Comfort, Indoor Air Quality and energy use.

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Model predictive control with linear models

Cited by 589 publications

References 32 publications

Complexity Reduction in Explicit MPC through Model Reduction

Complexity Reduction in Explicit MPC through Model Reduction

Reference Tracking MPC Using Dynamic Terminal Set Transformation

Model Predictive Control of Thermal Comfort and Indoor Air Quality in livestock stable

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