Modeling of linear parameter-varying systems using interpolation of root macromodels and scaling coefficients

Ferranti, Francesco; Rolain, Yves

doi:10.1016/j.ymssp.2015.01.007

Cited by 2 publications

(2 citation statements)

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“…Quasi-means that the scheduling parameters of the LPV model include system variables [19]. There are two main methods of LPV (or quasi-LPV) modeling which have been researched in the literature: the global method and the local method [20,21]. In the global method, the LPV model of the system can be derived from one experiment.…”

Section: Introductionmentioning

confidence: 99%

“…In the global method, the LPV model of the system can be derived from one experiment. Additionally, both control inputs and scheduling parameters are excited simultaneously in this experiment, which is not realizable in aero engine LPV modeling applications [20]. This is because the dynamic of the engine is a continuous function of the wide flying envelope and the various operation states, which can hardly be covered by finite-time experiments.…”

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%