Robust H‘ Design of an Automotive Cruise Control System

Németh, Balázs; Gáspár, Péter; Orjuela, Rodolfo; Basset, Michel

doi:10.1016/j.ifacol.2015.10.049

Cited by 7 publications

(1 citation statement)

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“…The methodology was validated through simulations and experiments, proving its effectiveness in enhancing safety and performance. A controller for a longitudinal ADAS system of a test vehicle was designed in [59]. A robust H ∞ cruise control based on a feedforward and a feedback control design was proposed to ensure a precise velocity tracking and robustness against longitudinal disturbance effects such as aerodynamic forces, mass parameter variations, rolling resistance, and road slopes of the vehicle.…”

Section: Longitudinal Control Of Autonomous Vehicles 221 Longitudinal...mentioning

confidence: 99%

Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey

Rizk,

Chaibet,

Kribèche

2023

Applied Sciences

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Autonomous driving has the potential to revolutionize mobility and transportation by reducing road accidents, alleviating traffic congestion, and mitigating air pollution. This transformation can result in energy efficiency, enhanced convenience, and increased productivity, as valuable driving time can be repurposed for other activities. The main objective of this paper is to provide a comprehensive technical survey of the latest research in the field of lateral, longitudinal, and integrated control techniques for autonomous vehicles. The survey aims to explore a wide range of techniques and methodologies employed to achieve precise steering control while also considering longitudinal aspects. Model-based control techniques form the foundation for control, utilizing mathematical models of vehicle dynamics to design controllers that effectively track desired speeds and/or steering behavior. Unlike model-free control techniques such as reinforcement learning and deep learning algorithms facilitate the integration of longitudinal and lateral control by learning control policies directly from data and without explicit knowledge of the underlying dynamics. Through this survey, the paper delves into the strengths, limitations, and advancements in both model-based and model-free control approaches for autonomous vehicles. It investigates their performance in real-world scenarios and addresses the technical challenges associated with their implementation. These challenges may include uncertainties in the environment, adaptability to dynamic conditions, robustness, safety considerations, and computational complexity.

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Section: Longitudinal Control Of Autonomous Vehicles 221 Longitudinal...mentioning

confidence: 99%