H /sub ∞/ adaptive, PID and fuzzy control: a comparison of controllers for vehicle lane keeping

Chaib, Salim; Netto, Mariana; Mammar, Saïd

doi:10.1109/ivs.2004.1336370

Cited by 65 publications

(47 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are other techniques for controlling steering, such as Hoo, Adaptive, and PID, as described by Chaib etal. [11]. Other real vehicle applications have been developed that are capable of autonomous steering management and performing human-like tracking [12]- [14].…”

Section: Lane-change Fuzzy Control In Autonomousmentioning

confidence: 99%

Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver

Naranjo

González²,

Garcia³

et al. 2008

IEEE Trans. Intell. Transport. Syst.

357

144

View full text Add to dashboard Cite

Abstract-The automation of the overtaking maneuver is considered to be one of the toughest challenges in the development of autonomous vehicles. This operation involves two vehicles (the overtaking and the overtaken) cooperatively driving, as well as the surveillance of any other vehicles that are involved in the maneuver. This operation consists of two lane changes-one from the right to the left lane of the road, and the other is to return to the right lane after passing. Lane-change maneuvers have been used to move into or out of a circulation lane or platoon; however, overtaking operations have not received much coverage in the literature. In this paper, we present an overtaking system for autonomous vehicles equipped with path-tracking and lane-change capabilities. The system uses fuzzy controllers that mimic human behavior and reactions during overtaking maneuvers. The system is based on the information that is supplied by a high-precision Global Positioning System and a wireless network environment. It is able to drive an automated vehicle and overtake a second vehicle that is driving in the same lane of the road.Index Terms-Fuzzy control, hybrid control, intelligent control, proportional-integral differential (PID) control, road vehicle control.

show abstract

Section: Lane-change Fuzzy Control In Autonomousmentioning

confidence: 99%

Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver

Naranjo

González²,

Garcia³

et al. 2008

IEEE Trans. Intell. Transport. Syst.

357

144

View full text Add to dashboard Cite

show abstract

“…Chaib et al describe in [12] the work done on automatic lateral control within the French Institut National De Recherche sur les Transports et Leur Securite (INRETS)/Laboratoire Central des Ponts et Chaussées (LCPC) Laboratoire sur les Interactions Véhicules-Infrastructure-Conducteurs (LIVIC), comparing H∞, adaptive, proportional-integral differential (PID), and fuzzy controllers, and outlining the advantages and drawbacks of using these techniques. They have all been tested on real vehicles.…”

Section: Introductionmentioning

confidence: 99%

Power-Steering Control Architecture for Automatic Driving

Naranjo¹,

González²,

Garcia³

et al. 2005

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Abstract-The unmanned control of the steering wheel is, at present, one of the most important challenges facing researchers in autonomous vehicles within the field of intelligent transportation systems (ITSs). In this paper, we present a two-layer control architecture for automatically moving the steering wheel of a mass-produced vehicle. The first layer is designed to calculate the target position of the steering wheel at any time and is based on fuzzy logic. The second is a classic control layer that moves the steering bar by means of an actuator to achieve the position targeted by the first layer. Real-time kinematic differential global positioning system (RTK-DGPS) equipment is the main sensor input for positioning. It is accurate to about 1 cm and can finely locate the vehicle trajectory. The developed systems are installed on a Citroën Berlingo van, which is used as a testbed vehicle. Once this control architecture has been implemented, installed, and tuned, the resulting steering maneuvering is very similar to human driving, and the trajectory errors from the reference route are reduced to a minimum. The experimental results show that the combination of GPS and artificial-intelligence-based techniques behaves outstandingly. We can also draw other important conclusions regarding the design of a control system derived from human driving experience, providing an alternative mathematical formalism for computation, human reasoning, and integration of qualitative and quantitative information.

show abstract

“…The aim of the lateral control is to keep the vehicle between lanes [3,4]. Various lateral control methods have been studied for autonomous lane keeping system (LKS) [5][6][7][8][9]. In LKS, the reference steering wheel angle is derived by the lane-keeping control method.…”

Section: Nomenclaturementioning

confidence: 99%

“…The previous lateral control methods regarded the steering wheel angle as the system input [5][6][7][8][9]. These cases may use a DC motor instead of the power steering system or modify the EPS system for the steering wheel angle control.…”

confidence: 99%

Sliding Mode Control for an Electric Power Steering System in an Autonomous Lane Keeping System

Yu¹,

Kim²,

Son³

et al. 2015

Journal of Institute of Control, Robotics and Systems

View full text Add to dashboard Cite

Abstract:In this paper, we develop a sliding mode control for steering wheel angle control based on torque overlay in order to resolve the problem of previous methods for Electric Power Steering (EPS) systems in the Lane Keeping System (LKS) of autonomous vehicles. For the controller design, we propose a 2nd order model of the electric power steering system in an autonomous LKS. The desired state model is designed to prevent a rapid change of the steering wheel angle. The sliding mode steering wheel angle controller is developed for the robustness of the disturbance. Since the proposed method is designed based on torque overlay, torque integration with basic functions of the EPS system for the steering wheel angle control is available for the driver's convenience. The performance of the proposed method was validated via experiments.Keywords: electric power steering, torque overlay, sliding mode control

show abstract

H /sub ∞/ adaptive, PID and fuzzy control: a comparison of controllers for vehicle lane keeping

Cited by 65 publications

References 1 publication

Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver

Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver

Power-Steering Control Architecture for Automatic Driving

Sliding Mode Control for an Electric Power Steering System in an Autonomous Lane Keeping System

Contact Info

Product

Resources

About