New adaptive approaches to real-time estimation of vehicle sideslip angle

You, Seungkwon; Hahn, Jin-Oh; Lee, Hyeongcheol

doi:10.1016/j.conengprac.2009.07.002

Cited by 105 publications

(64 citation statements)

References 16 publications

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“…Indeed, this model is sufficiently accurate as it accounts for the main features such as the vertical load v F , slip angle , slip coefficient . On the other hand, it has already proved to be useful in designing simple estimators for state variables like slip angle and lateral efforts (You et al, 2009). In the present paper, this model will prove to be useful in control design.…”

Section: Kiencke's Tire Modelingmentioning

confidence: 85%

“…In this respect, several works have been devoted to what is commonly referred to adaptive cruise control that consists in maintaining a specified headway between vehicles (Ioannou and Chien, 1993;Moon et al, 2009). Different control techniques have been used in these works including linear and adaptive control (You et al, 2009), genetic fuzzy control (Poursamad and Montazeri, 2008), sliding mode control (Liang et al, 2003;Nouveliere and Mammar, 2007), and scheduling gain control involving PIDs (Ren et al, 2008). However, most previous works on longitudinal control were based on simple models neglecting important nonlinear aspects of the vehicle such as rolling resistance, aerodynamics effects and road load.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle longitudinal motion modeling for nonlinear control

Majdoub

Giri

Ouadi

et al. 2012

Control Engineering Practice

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Abstract-The problem of modeling and controlling vehicle longitudinal motion is addressed for front wheel propelled vehicles. The chassis dynamics are modeled using relevant fundamental laws taking into account aerodynamic effects and road slop variation. The longitudinal slip, resulting from tire deformation, is captured through Kiencke"s model. A highly nonlinear model is thus obtained and based upon in vehicle longitudinal motion simulation. A simpler, but nevertheless accurate, version of that model proves to be useful in vehicle longitudinal control. For security and comfort purpose, the vehicle speed must be tightly regulated, both in acceleration and deceleration modes, despite unpredictable changes in aerodynamics efforts and road slop. To this end, a nonlinear controller is developed using the Lyapunov design technique and formally shown to meet its objectives i.e. perfect chassis and wheel speed regulation.

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Section: Kiencke's Tire Modelingmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Vehicle longitudinal motion modeling for nonlinear control

Majdoub

Giri

Ouadi

et al. 2012

Control Engineering Practice

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“…Concerning the implementation of the bicycle model shown in (1), the cornering stiffness f C and r C henceforth denote those obtained by the cornering stiffness adaptation scheme dealt in [12]. The lateral acceleration is expressed as the following.…”

Section: B Bicycle Model-based Observermentioning

confidence: 99%

Real-Time Offset Error Compensation of 6D IMU Mounted on Ground Vehicles Using Disturbance Observer

Oh¹,

Noh²,

Choi³

2013

JACN

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Abstract-This paper mainly deals with the offset error compensation algorithm related with the 6D IMU (inertial measurement unit) that measures the linear accelerations and angular velocities about the longitudinal, lateral, and vertical axis of ground vehicles. It is assumed that the independent wheel speed data and steering wheel angle information are provided for the sensor compensation algorithm. Using a disturbance observer, through designing a linear model and inverse model of the vehicle motion, the offset errors of the accelerometers are estimated. The stability of the entire compensational system is verified, and finally, the performance of the suggested algorithm is tested based on a well-known vehicle dynamics simulation tool, CarSim.Index Terms-Bicycle model, disturbance observer, inertial measurement unit, offset compensation, signal processing.

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“…Considering the previous criteria and signals employed in typical sideslip angle observers [14,15], three groups of input data have been selected:  Yaw rate/Longitudinal velocity.…”

Section: Input Datamentioning

confidence: 99%

“…The modeling of sideslip angle is complex due to the non-linear dynamics of the vehicle. Some authors employ physical models for the estimation of sideslip angle [12,13,14,15]. Most mentioned methods are based on the bicycle dynamic model or its variations.…”

Section: Introductionmentioning

confidence: 99%