Active Front Steering (Part 1): Mathematical Modeling and Parameter Estimation

Klier, Willy; Reinelt, Wolfgang

doi:10.4271/2004-01-1102

Cited by 31 publications

(14 citation statements)

References 2 publications

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“…The complete system setup including mathematical modelling and parameter estimation is described in great detail in [4]. In order to make this paper self-contained, the basic relations are given here as well.…”

Section: System Description and Notationmentioning

confidence: 99%

On-Line Sensor Monitoring in an Active Front Steering System Using Extended Kalman Filtering

Reinelt¹,

Lundquist²,

Johansson³

2005

SAE Technical Paper Series

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Section: System Description and Notationmentioning

confidence: 99%

On-Line Sensor Monitoring in an Active Front Steering System Using Extended Kalman Filtering

Reinelt¹,

Lundquist²,

Johansson³

2005

SAE Technical Paper Series

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“…Oraby developed an AFS control strategy that was based on optimal control theory using the linear quadratic regulator (LQR) technique [3]. Klier and Reinelt designed a mathematical model of an AFS system including eight rigid bodies, 46 constraint relations, and a synchronous motor [2]. Zhang et al developed a quantitative feedback theory (QFT) and evaluated the control system under various emergency maneuvers and road conditions [4].…”

Section: Introductionmentioning

confidence: 99%

“…Both angles result in a pinion angle at the steering rack. The resulting roadwheel angle can then be calculated via the pinion angle, as well as a static nonlinearity that accounts for the relationship between the pinion angle and rack displacement for the steering geometry [2].…”

Section: Introductionmentioning

confidence: 99%

Design and Comparison of AFS Controllers with PID, Fuzzy-Logic, and Sliding-Mode Controllers

Song

2013

Advances in Mechanical Engineering

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Three active front-steering (AFS) controllers were developed to enhance the lateral stability of a vehicle. They were designed using proportional-integral-derivative (PID), fuzzy-logic, and sliding-mode control methods. The controllers were compared under several driving and road conditions with and without the application of braking force. A 14-degree-of-freedom vehicle model, a sliding-mode antilock brake system (ABS) controller, and a driver model were also employed to test the controllers. The results show that the three AFS controllers allowed the yaw rate to follow the reference yaw rate very well, and consequently the lateral stability improved. On a split-road, the controllers forced the vehicle to proceed straight ahead. The results also verify that the driver model can sufficiently control the vehicle to allow it to follow a desired path.

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“…Consequently, for safety critical systems it is often required to implement Riemann sampling at fast periodic sample rates to insure adequate stability margins in the presence of noise and parametric uncertainty. For digital communication between sensors, the signal is represented by a sequence (f 0 , t 0 ), (f 1 , t 1 …”

Section: A Signal Samplingmentioning

confidence: 99%

A New Multisensor Network for Collision Avoidance and Jackknife Prevention of Articulated Vehicles using Lebesgue Sampling

McCann¹,

Le²

2005 IEEE Vehicle Power and Propulsion Conference

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Sensor networks are increasingly used in advanced vehicle and transportation applications. It is desirable in many systems to minimize sensor power requirements. Motivated by the flexibility available with battery powered wireless sensors, this paper presents a new sensor network communication method that significantly reduces the occurrence of message transmissions. This is achieved by applying Lebesgue sampling theory to detect transitions in signal levels between quantized states. Sensor outputs are broadcasted only when significant information has been acquired as a state transition. Consequently, power consumed throughout the sensor network is minimized. Results for a large truck vehicle stability enhancement system are given. The feedback control system is tested in a laboratory system with an SAE-J1939 compliant commercial vehicle network, with a virtual truck implementation for evaluating the effectiveness of the sensor network technique.

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Active Front Steering (Part 1): Mathematical Modeling and Parameter Estimation

Cited by 31 publications

References 2 publications

On-Line Sensor Monitoring in an Active Front Steering System Using Extended Kalman Filtering

On-Line Sensor Monitoring in an Active Front Steering System Using Extended Kalman Filtering

Design and Comparison of AFS Controllers with PID, Fuzzy-Logic, and Sliding-Mode Controllers

A New Multisensor Network for Collision Avoidance and Jackknife Prevention of Articulated Vehicles using Lebesgue Sampling

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