Model predictive control with state estimation

Ricker, N. Lawrence

doi:10.1021/ie00099a013

Cited by 159 publications

(88 citation statements)

References 11 publications

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“…They showed how open-loop and closed-loop observers can be incorporated into the predictive control framework to improve regulatory control of MPC. Ricker (1990) showed how an MPC algorithm similar to the conventional MPC techniques can be developed based on a general state-space model. Lee et al (1992a) proposed a state-space MPC technique applicable to general mult i-rate sampled-data systems.…”

Section: Introductionmentioning

confidence: 99%

State-Space Interpretation of Model Predictive Control

Lee

Morari

García

1995

Methods of Model Based Process Control

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A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal prediction vector without introducing significant additional numerical complexity. In the case of integrated or double integrated white noise disturbances filtered through general first-order dynamics and white measurement noise, the optirnal filter gain is parametrized explicitly

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Section: Introductionmentioning

confidence: 99%

State-Space Interpretation of Model Predictive Control

Lee

Morari

García

1995

Methods of Model Based Process Control

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“…How DMC evolved to become what today is called MPC together with the current status of research in MPC is described in numerous surveys, e.g. (Garcia et al, 1989;Morari and Lee, 1991;Morari and Lee, 1999;Muske and Rawlings, 1993;Qin and Badgwell, 1996;Ricker, 1991).…”

mentioning

confidence: 99%

Efficient Solution of Second Order Cone Program for Model Predictive Control

Åkerblad

Hansson

2002

IFAC Proceedings Volumes

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“…The MPC strategy adopted in this paper goes back to [6], [7], [8]. In [6], an algorithm referred to as LQG-MPC was proposed to deal with the state and control linear inequality constraints.…”

Section: B Related Workmentioning

confidence: 99%

“…In [6], an algorithm referred to as LQG-MPC was proposed to deal with the state and control linear inequality constraints. In [7], [8], the Quadratic Dynamic Matrix Control is used to solve nonlinear process optimization with state estimation.…”

Section: B Related Workmentioning

confidence: 99%

Multi-scale stochastic optimization for Home Energy Management

Jia

Murphy-Hoye

et al. 2011

2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Abstract-The problem of scheduling and control of appliances for Home Energy Management (HEM) is considered. A multitime scale and multi-stage stochastic optimization framework is proposed for the control of the Heating, Ventilation, and Air Conditioning (HVAC) unit, the charging of Plug-in Hybrid Electric Vehicle (PHEV), and the scheduling of deferrable load such as washer/dryer operations. Formulated as a constrained stochastic optimization that incorporates thermal dynamics, temperature measurements, and the real time pricing signal, a model predictive control algorithm is proposed that minimizes customer's discomfort level subject to cost and peak power constraints.

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Model predictive control with state estimation

Cited by 159 publications

References 11 publications

State-Space Interpretation of Model Predictive Control

State-Space Interpretation of Model Predictive Control

Efficient Solution of Second Order Cone Program for Model Predictive Control

Multi-scale stochastic optimization for Home Energy Management

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