Stability of neural-network based train cruise advisory control with aperiodical measurements
نویسندگان
چکیده
Abstract In this paper, a neural-network based driver advisory train cruise control system is considered. The controller assists the with signals by considering and driver’s actual state information (attention fatigue) measured dedicated sensors. Considering delays in sensor measurements, paper aims to assess closed-loop stability of driver-in-the-loop control. For purpose, model considered as time-varying system, includes rolling aerodynamic resistance forces be sampled-data three layer multi-layer perceptron. Further, aperiodic measurement problem approached analysis delayed system. Based on recent developments design augemented Lyapunov Krasovskii Functional (LKF) using Bessel-Legendre inequality for delays, sufficiency conditions existence L2 driver-train terms solvable Linear Matrix Inequalities are provided. Further case study presented illustrate effectiveness proposed method.
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ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2021
ISSN: ['2405-8963', '2405-8971']
DOI: https://doi.org/10.1016/j.ifacol.2021.10.016