Abstract

Introduced by the authors, the hybrid electromagnetic brake (HEB) has considerable advantages over conventional friction and hybrid brakes. One of its advantages is the controllability of the brake system even when the HEB is integrated in a vehicle. Therefore, in this paper, robust speed and slip control schemes for HEB systems taking into account the brake and vehicle dynamics are developed for uncertain system parameters, and unknown external disturbance conditions, owing to neural networks learning and adaptation abilities. The presented robust control schemes exhibit advantages such as not requiring exact information about the brake and vehicle parameters for the controller design, and that the control algorithm is capable of efficiently tracking performance while ensuring the stability of the closed-loop system. The controllers are suitable for many vehicle active safety control systems such as, adaptive cruise control, anti-lock braking systems, electronic stability control, rollover prevention and autonomous vehicle operations. Both simulations and experiments are presented to show the controllers performances and the effectiveness of the presented control schemes.

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