Data-driven linear parameter-varying modelling of the steering dynamics of an autonomous car

Rödönyi, Gábor; Tóth, Roland; Pup, Dániel; Kisari, Ádám; Vigh, Zsombor; Kőrös, Péter; Bokor, József

doi:10.1016/j.ifacol.2021.08.575

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…In a data-driven modeling method, only the input, state and output trajectory information are used to identify the parameters [31]. The least square criterion and gradient descent algorithm are adopted to optimize the parameters of the LPV model [31][32][33]. A statistical approach was used in data-driven LPV system identification in [34].…”

Section: Introductionmentioning

confidence: 99%

“…A statistical approach was used in data-driven LPV system identification in [34]. However, the previously mentioned data-driven modeling methods are inefficient because they must be carried out offline [31,32,34] or carry online identification at the cost of reduced accuracy with the stochastic gradient descent algorithm. For aero engines, a more engaging, newly developed data-driven modeling method is the equilibrium manifold expansion (EME) model [35][36][37].…”

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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