A new online modelling method for aircraft engine state space model

Pang, Shuwei; Li, Qiuhong; Zhang, Haibo

doi:10.1016/j.cja.2020.01.011

Cited by 15 publications

(9 citation statements)

References 42 publications

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“…Note that n g and n p were already chosen as the states, so its corresponding entries in C and D had a constant value of 0 and 1. For P 3 and T 44 , corresponding entries in matrix C and D varied with the scheduling parameter [15].…”

Section: Lpv Model Descriptionmentioning

confidence: 99%

“…LPV models preserve the advantageous properties of LTI models, such as low modeling cost, while being able to represent a large class of nonlinear systems with high accuracy [11]. Therefore, the LPV model has been widely used in convex system controller design [12,13] and model-based approaches [14,15], such as robust control, fault diagnosis and optimal control.…”

Section: Introductionmentioning

confidence: 99%

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

Pang

Zhou

et al. 2022

Energies

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The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model’s scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated.

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Section: Lpv Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pang

Zhou

et al. 2022

Energies

Self Cite

View full text Add to dashboard Cite

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“…The widely used polynomial models are ARX, ARMAX, OE, and BJ [33,34,35]. The general representation of the polynomial model is represented using (2)…”

Section: Models Entitymentioning

confidence: 99%

“…In equation (33), the covariance matrix P (i) (k+1) and future regressor vector φ (i) (k + 1) are the functions of u(k). As a result, the optimization objective rewards inputs that reduce the future covariance P (i) (k + 1).…”

Section: ) On-line Parameter Estimation For Arx Modelmentioning

confidence: 99%

Dual Adaptive Model Predictive Controller Application to Vertical Roller Mill Process Used in the Cement Industry

Jayalalitha

2020

IEEE Access

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“…3.5% growth annually in the next decade. Modeling technology is one of the most effective technologies in aero-engine simulation, and can be used for sensor fault diagnosis, control system design, and performance degradation estimation [6][7][8][9]. During the past few decades, there have been tremendous studies on aero-engine modeling and simulation technologies, among which the most typical method is the modelbased method.…”

Section: Introductionmentioning

confidence: 99%

A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine

Huang¹,

Sun²,

Tao³

et al. 2022

Meas. Sci. Technol.

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Sensor fault diagnosis and performance degradation estimation (SFDPDE) play a critical role in the operation and maintenance of aero-engine. In this study, a modified fusion model driven by sensor measurements is proposed to overcome the drawbacks of single data-driven and single model-based methods. Two types of on-board models are established based on augmented state space equations, and a data-driven model based on extreme learning machine (ELM) is constructed for residual correction of the on-board model. A bidirectional information transmission algorithm is designed in the SFDPDE framework in order to include the function coordination. Kalman filter (KF) is employed as the optimal algorithm in the SFDPDE framework containing the standardized sensor parameter selection process. The experimental results indicate that, the proposed fusion model improves the accuracy of sensor fault diagnosis, reduces the mean square error (MSE) of health parameter (HP) estimations, while the information sharing module expands the application scope of SFDPDE and improves its accuracy as well as stability.

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A new online modelling method for aircraft engine state space model

Cited by 15 publications

References 42 publications

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

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

Dual Adaptive Model Predictive Controller Application to Vertical Roller Mill Process Used in the Cement Industry

A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine

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