Development of a neuromuscular driver model with an estimation of steering torque feedback in vehicle steer-by-wire systems

Gholami, Amir Hossein; Majidi, Majid

doi:10.1177/1464419319829980

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Compared with the traditional mechanical system, the steer-by-wire system has many advantages, such as the further improvement of vehicle safety performance, driving characteristics and maneuverability, vehicle comfort and environmental protection. 1–5…”

Section: Introductionmentioning

confidence: 99%

Design and experimental implementation of observer-based adaptive neural network steering control for automated vehicles

Luo

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this paper, an observer-based adaptive neural network controller is developed for the Steer-by-Wire (SbW) system of automated vehicles with uncertain nonlinearity and unmeasured state. An observer is introduced to estimate the angular velocity of the front wheels, so the hardware cost and the complexity of mechanical structure and electronic circuits are reduced. Then, an observer-based adaptive neural network controller is proposed for the SbW system to achieve excellent steering precision. A radial basis function (RBF) neural network is used to model the uncertain nonlinearity, which mainly includes self-aligning torque and unknown friction torque with strong nonlinearity. The adaptive law of the RBF neural network designed by fuzzy basis functions rather than by its filtering is derived by Lyapunov stability theory and the Strictly Positive Real (SPR) condition. The tracking error and the observation error can be guaranteed to converge asymptotically to zero. Simulation and experimental results for two paths highlight the effectiveness of the proposed control algorithm.

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Section: Introductionmentioning

confidence: 99%