Vehicle stability control by using an adaptive sliding-mode algorithm

Le, Anh Tuan; Chen, Chih Keng

doi:10.1504/ijvd.2016.080016

Cited by 8 publications

(4 citation statements)

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“…Simulations were conducted in the MATLAB and Simulink environment and linked to the CarSim software. The CarSim vehicle model was a full sedan; the parameters of its tires and aerodynamics, such as sprung mass, powertrain, and suspension, were shown in my previous study [1]. In this study, the process and measurement noise autocovariance matrices were selected as follows:…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Dugo's tire model enables calculating longitudinal and lateral forces in a closed form, which is required for implementing the simulation. The vehicle simulation parameters were described in the previous studies [1] and [13].…”

Section: Vehicle-model Vericationmentioning

confidence: 99%

“…Modern intelligent automotive systems are equipped with a vehicle stability control (VSC), which is intended to enhance the active safety and handling predictability for drivers [1]. VSCs increase driver control on curves and slippery roads, substantially reducing the risk of fatal crashes.…”

Section: Introductionmentioning

confidence: 99%

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Application of Extended Kalman Filtering for Estimating Immeasurable Vehicle State Variables

Anh

2017

J. ADV. ENG. COMPUT.

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This paper presents an extended Kalman filtering (EKF) algorithm for estimating immeasurable state variables of a vehicle stability control system. Initially, the steering angle and vertical forces on the tires were considered inputs of the estimator. The measured process outputs were the sensor signals egarding longitudinal and lateral accelerations, steering angle, yaw rate, and wheel speed. Subsequently, by using Euler discretization for a seven-degree-of-freedom nonlinear vehicle model, difficult-to-measure state variables such as lateral velocity, vehicle side-slip angle, and lateral tire forces were identified separately by using the EKF algorithm. The estimation results of the proposed control system evidenced high performance. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Vehicle-model Vericationmentioning

confidence: 99%

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Application of Extended Kalman Filtering for Estimating Immeasurable Vehicle State Variables

Anh

2017

J. ADV. ENG. COMPUT.

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“…The weight factors of tracking errors and control inputs in the cost function were online updated according to a vehicle stability index. Le et al [23] proposed an algorithm for a vehicle stability control system, which was a combination of SMC and parameter adaptation. The method guaranteed transient performance from SMC-based controller for both parametric and model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Stability Enhancement through Hierarchical Control for a Four-Wheel-Independently-Actuated Electric Vehicle

Wang

Zhang

et al. 2017

Energies

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In this paper, an optimal control strategy for a four-wheel-independently-actuated electric vehicle (FWIA EV) is proposed to improve vehicle dynamics stability and handling performance. The proposed scheme has a hierarchical structure composed of an upper and a lower controller. The desired longitudinal and lateral forces and yaw moment are determined based on the sliding-mode control (SMC) scheme in the upper controller, which takes the longitudinal and lateral velocity and the yaw rate as control variables. In the lower controller, an optimization algorithm is adopted to allocate the driving/braking torques to each in-wheel motor. A cost function with adjustable weight coefficients is specially designed by taking the motor power capability and the tire workload into consideration. The simulation and hardware-in-loop experimental results show that the proposed control strategy exhibits superior performance in comparison to commonly-used rule-based control strategies, and has the capability of online implementation.

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Vehicle Stability Controller Based on Adaptive Sliding Mode Algorithm with Estimated Vehicle Side-Slip Angle

2016

AETA 2016: Recent Advances in Electrical Engineering and Related Sciences

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Vehicle stability control by using an adaptive sliding-mode algorithm

Cited by 8 publications

References 0 publications

Application of Extended Kalman Filtering for Estimating Immeasurable Vehicle State Variables

Application of Extended Kalman Filtering for Estimating Immeasurable Vehicle State Variables

Vehicle Stability Enhancement through Hierarchical Control for a Four-Wheel-Independently-Actuated Electric Vehicle

Vehicle Stability Controller Based on Adaptive Sliding Mode Algorithm with Estimated Vehicle Side-Slip Angle

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