Improved model predictive control for the flow field in a wind tunnel with model uncertainty

Liu, Yanming; Wang, Junjie; Liu, Xiaotao; Li, Xiaoping; Yao, Bo; Song, Lihao

doi:10.1016/j.jfranklin.2019.12.004

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…Similar observer‐type NPC can also be found in References 26 and 27. Using indirect feedback, NPC strategies are researched for discrete‐time linear systems in References 28 and 29. The authors in Reference 30 propose an event‐triggered NPC for nonlinear networked systems.…”

Section: Introductionmentioning

confidence: 90%

Predictor‐based switching control for networked systems

Cui

2023

Intl J Robust & Nonlinear

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SummaryThis paper concerns a networked predictive control (NPC) for linear systems with data time‐varying delays, packet losses and disordering. By utilizing the received time‐delayed state measurement and control input, a predictive algorithm is firstly proposed for approximating the future state prediction. Afterwards, a probability dependent switching control law is proposed. Although the large data transmission delays emerge in the forward and backward channels, the packet disordering in the backward channel can be naturally excluded thus the actuator always receives valid control command during each sampling period with correct time sequence. The proposed NPC not only guarantees the global uniform exponential stability of the overall networked system but also brings merits in tolerating locally unstable sub‐systems. Numerical examples are provided to demonstrate the effectiveness and improvements of the proposed method.

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Section: Introductionmentioning

confidence: 90%

Predictor‐based switching control for networked systems

Cui

2023

Intl J Robust & Nonlinear

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“…In regression based outlier detection models, nevertheless, the dynamics of the controlled plant has not been concerned since we only aim to mine observations that do not conform the normal pattern described by the relationship between predictors and the response variable. Accordingly, the regression in our outlier detection framework can be represented by Formula (3).…”

Section: B Different Model Structuresmentioning

confidence: 99%

“…During recent years, model predictive control (MPC) has been successfully applied to the control of Mach number in practical wind tunnels, due to its better performance than PID algorithm on controlled plants with large delay and frequent disturbance [1][2][3][4]. Note that in MPC an accurate predictive model is always desired, although the process of feedback correction can compensate the loss triggered by biased predictions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

et al. 2021

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Model predictive control (MPC) has been successfully applied in practical wind tunnel systems. As a critical component of MPC, the accurate prediction of Mach number becomes hence very significant. Real measurement data records often contain outliers or noisy data because of the harsh working conditions of practical systems. Such datasets are detrimental to most data-driven Mach number predictors. This paper focuses on improving Mach number prediction via a regression-based outlier detection framework. The development of a regression-based outlier detector for enhancing Mach number prediction is natural since only regression analysis needs to be implemented for both prediction and outlier detection. In contrast to existing regression-based detectors, we have taken into account three problems that may greatly impact the detecting performance. Specifically, we use Bagging technique to reduce the negative influence of unknown outliers during the training phase. Then, the notion of extreme value analysis is used to determine an appropriate threshold for the calculated outlier scores. A robust scaling method based on Hampel identifier is also used to alleviate the influence of outliers on data scaling. Several datasets stemming from a real-world wind tunnel are used in experiments to verify the effectiveness of our detector. The experimental results show that regression-based outlier detectors often perform better if the prior knowledge about the dependent variable is used. In addition, the proposed detector has outperformed other regression-based detectors.

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“…Similarly, Letchford et al [ 12 ] conducted theoretical analysis and mathematical modelling of wind loads on the sign boards to simulate the wind load in the natural environment. To analyse the action mechanism, Liu et al [ 13 ] used mathematical modelling methods to improve the model of wind tunnel experiments. They also set up a wind tunnel laboratory where the actual structures reduced by a certain proportion were replicated and placed in the wind tunnel to conduct wind pressure testing and obtain experimental data to verify the accuracy of the turbulence model.…”

Section: Introductionmentioning

confidence: 99%

CFD simulation study on wind load of perforated traffic sign board

Zeng

et al. 2020

PLoS ONE

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Traffic sign boards are often blown away by strong winds, seriously endangering the safety of vehicles and pedestrians. To increase their resistance to strong winds, sign boards are perforated. Using computational fluid dynamics simulations, the wind load resistance of traffic signs with holes was optimised. By comparing the solutions to different turbulence models with empirical results, it was found that the simulation results of the re-normalisation group (RNG) model have the smallest error. Therefore, the RNG model is used to simulate the wind load of traffic sign boards with different perforation diameters and different hole spacings under different wind speeds. By analysing the wind pressure distribution on the surface of the perforated traffic sign board, the perforation scheme for different regions of the sign board under different wind loads was obtained. The results show that reasonable perforation diameters and hole spacings can reduce the wind load and improve the wind load resistance of sign boards. This study provides decision-makers with useful information for installing traffic signs in areas affected with strong winds, thereby improving the wind resistance of traffic signs and ensuring traffic safety.

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Improved model predictive control for the flow field in a wind tunnel with model uncertainty

Cited by 9 publications

References 10 publications

Predictor‐based switching control for networked systems

Predictor‐based switching control for networked systems

Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

CFD simulation study on wind load of perforated traffic sign board

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