An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines

Gu, Z.; Pang, Shuwei; Zhou, Wenxiang; Li, Yuchen; Li, Qiuhong

doi:10.3390/en15041255

Cited by 15 publications

(11 citation statements)

References 43 publications

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“…Remark 1. Comparing the laws of the MRAC method in equation (17) with the laws of the PMRAC method in equation (35), it is evident that the presence of the state predictor in equation ( 17) adds the low-pass filtering effects of the predictor dynamics to the direct MRAC laws.…”

Section: Model Of the Jetcat Spt5 Turboshaft Enginementioning

confidence: 99%

“…A novel online data-driven linear parameter-varying modeling method has been proposed based on online sequential learning machine with an additional multiplying layer in a gas turbine engine. 17 Estimation and prediction of the future values of gas turbine sensors have been investigated using a linear regression model. 18 Design of a new algorithm that projects the thrust of the turbine is proposed by Stotsky.…”

Section: Introductionmentioning

confidence: 99%

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Control of a jet engine using predictor-based adaptive strategy in different flight modes

Ranjbar

Khosravi

Ahmadian

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a predictor-based model reference adaptive control method is proposed for a JetCat SPT5 turboshaft engine in full thrust, cruise, and idle modes. In the predictor-based model reference adaptive control method, in addition to the tracking error, the predictor error is also utilized in adaptive laws to achieve the desired control objectives, such as improving the tracking performance and control signals, and reducing the tracking error. The proposed method is implemented on a turboshaft engine system with actual dynamic values. First, three properly separated equilibrium points are selected on the nominal plant equilibrium manifold to linearize the plant model at the equilibrium points. Then, the predictor-based model reference adaptive control controller is designed and investigated for the three equilibrium points generating three operating modes of the engine, that is, full thrust, cruise, and idle. The stability of the control system is proved by the Lyapunov method. To evaluate the efficiency of the state predictor method in the simulation scenarios, the proposed method is compared with the classic model reference adaptive control method. The simulation results illustrate the superiority of the predictor-based model reference method due to the reduction of unwanted fluctuations in tracking performance, faster convergence of the tracking error to zero, and smoother control signals for all the equilibrium points.

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Section: Model Of the Jetcat Spt5 Turboshaft Enginementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Control of a jet engine using predictor-based adaptive strategy in different flight modes

Ranjbar

Khosravi

Ahmadian

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…With incremental learning, the above problems can be solved. In recent years, Online Sequential Extreme Learning Machine (OS-ELM) [27] has attracted wide attention in incremental learning and has been widely applied in online modeling of practical problems [28][29][30][31]. Yin et al [28] established an online sequential extreme learning machine classification model and used it for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

On-line Evolutionary Identification Technology for Milling Chatter of Thin Walled Parts Based on the Incremental-Sparse K-Means and the Online Sequential Extreme Learning Machine

Wang¹,

Yue

Liu³

et al. 2023

Preprint

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In the milling process of thin-walled parts, chatter is very easy to occur, which has a very adverse impact on the surface quality and machining efficiency of the workpiece. In order to solve the problem of low accuracy of milling state identification caused by few initial samples and dynamic changes in the milling process, a hybrid online evolutionary chatter identification model combining unsupervised learning and supervised learning is proposed. First of all, aiming at the problem that traditional K-Means algorithm is difficult to adapt to online dynamic clustering of milling chatter, an online Incremental-Sparse K-Means algorithm (ISK-Means) was proposed, and the dynamic incremental-sparse strategy of K-Means was designed. Secondly, aiming at the problem that the Online Sequential Extreme Learning Machine (OS-ELM) algorithm directly adds its predicted samples to the training sample set during the incremental learning process, and the pseudo-samples in the training sample set would lead to the degradation of the OS-ELM model, a hybrid online evolutionary chatter identification model combining the ISK-Means and the OS-ELM was proposed, and the online identification and evolution strategy was designed.Finally, the experimental results show that the ISK-Means algorithm can greatly improve the clustering efficiency and is suitable for milling chatter online dynamic clustering. Meanwhile, compared with the existing model, the recognition accuracy of the hybrid online evolutionary chatter recognition model combined with ISK-Means algorithm and OS-ELM algorithm is improved by 1.31%. This is of great significance for the online control of subsequent chatter.

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“…Bai et al applied a long-short term memory (LSTM) network for fault detection of three-shaft marine gas turbine [29]. Online sequential extreme learning machines (OS-ELM) are used for data-driven engine modeling [30][31][32]. These studies underlined the suitability of Artificial Intelligence tools to predict engine performance with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Menga¹,

Mothakani²,

Giorgi³

et al. 2022

Preprint

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Micro turbojets are used for propelling radio-controlled aircraft, aerial targets and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional Artificial Neural Network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation has a higher impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and Exhaust Gas Temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.

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An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines

Cited by 15 publications

References 43 publications

Control of a jet engine using predictor-based adaptive strategy in different flight modes

Control of a jet engine using predictor-based adaptive strategy in different flight modes

On-line Evolutionary Identification Technology for Milling Chatter of Thin Walled Parts Based on the Incremental-Sparse K-Means and the Online Sequential Extreme Learning Machine

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

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