Multi-objective stability control algorithm of heavy tractor semi-trailer based on differential braking

Zong, Changfu; Zhu, Tianjun; Wang, Chang; Liu, Haizhen

doi:10.3901/cjme.2012.01.088

Cited by 39 publications

(28 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both dynamic models [16] and kinematics models [17][18][19][20][21] can be used as the basis for the obstacle avoidance model. The dynamic model can accurately provide the position and posture of the vehicle, but this model is very complicated.…”

Section: Obstacle Avoidance Modelmentioning

confidence: 99%

Obstacle Avoidance of Semi-Trailers Based on Nonlinear Model Predictive Control

Bai

Chen

Meng

et al. 2019

WEVJ

View full text Add to dashboard Cite

Obstacle avoidance is a core part of the autonomous driving of off-road vehicles, such as semi-trailers. Due to the long length of semi-trailers, the traditional obstacle avoidance controller based on the circumcircle model can ensure that there is no collision between the semi-trailer and the obstacle, but it also greatly reduces the passable area. To solve this problem, we propose a new obstacle avoidance model. In this model, the distance between the obstacle and the middle line of semi-trailers is used as the indicator of obstacle avoidance. Based on this model, we design a new obstacle avoidance controller for semi-trailers. The simulation results show that the proposed controller can ensure that no collision occurs between the semi-trailer and the obstacle. The minimum distance between the obstacle center and the semi-trailer body trajectory is greater than the sum of the obstacle radius and the safety margin. Compared with the traditional obstacle avoidance controller based on the circumcircle model, the proposed controller greatly reduces the error between the semi-trailer and the reference path during obstacle avoidance.

show abstract

Section: Obstacle Avoidance Modelmentioning

confidence: 99%

Obstacle Avoidance of Semi-Trailers Based on Nonlinear Model Predictive Control

Bai

Chen

Meng

et al. 2019

WEVJ

View full text Add to dashboard Cite

show abstract

“…Lateral stability is an important part of road vehicles with high center of gravity (CG), e.g., sport utility vehicles (SUVs) and trucks [1,2]. It is shown that rollover accidents account for about 80% of non-collision fatal crashes, among which 70% are associated with light trucks, including SUVs and pickups [3].…”

Section: Introductionmentioning

confidence: 99%

Design and Validation of Differential Braking Controllers for Sport Utility Vehicles Considering the Interactions of Driver and Control System

He¹

2018

EOIJ

View full text Add to dashboard Cite

This paper introduces the design and validation of a differential braking controller for sport utility vehicles (SUVs) with driver-in-the-loop real-time simulations. SUVs are designed with high ground clearance, which is a main reason for their high rollover rate. A nonlinear 3 degrees-of-freedom (DOF) SUV model is generated to design a differential braking controller. The desired states are determined using a 2-DOF bicycle model and the lane-keeping control results derived from vehicle velocity and road curvature. The actual vehicle states of the 3-DOF model may deviate from the desired ones. A sliding model controller (SMC) is designed to minimize the state error to improve the performance measures, e.g., yaw stability. The SMC controller designed in LabVIEW is integrated with a virtual SUV generated in CarSim for cosimulations. The controller is first examined in the emulated sine-with-dwell maneuver specified in FMVSS 126. The SUV performance depends not only on the control strategy, but also on its interaction with the human driver. To study the interaction of the driver and the controller, the overall system is simulated using driver-software-in-the-loop (DSIL) realtime simulations under a double-line-change (DLC) maneuver. The simulations show that, even equipped with the electronic stability control (ESC) system, the driver still plays an important role in the vehicle dynamics. The simulations demonstrate the effectiveness of the proposed differential braking controller, and the research discloses important interactions of driver and ESC system.

show abstract

“…Vehicle dynamics stability control system (VSC/ESC/ESP) can effectively prevent vehicle from spinning, drifting out and rolling over at the limits of handling [4]. VSC can generally be divided into three categories: VSC based on differential braking [3,[5][6][7][8][9][10]; VSC based on active steering [11][12][13]; VSC through integration of two or more chassis control systems [14][15][16]. Of all these approaches, active brake control has received the most attention, because it can be easily realized, and VSC based on differential braking takes full advantage of the longitudinal force margin of the tire to generate active yaw moment to keep the vehicle stable under extreme driving maneuvers [3,9,14].…”

Section: Introductionmentioning

confidence: 99%

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

et al. 2017

Vehicle System Dynamics

View full text Add to dashboard Cite

Modeling uncertainties, parameter variations and unknown external disturbances are the main obstacles in the development of advanced controller for vehicle stability control at the limits of handling. For the parameter variations and unknown external disturbances, sliding mode control (SMC) method could be employed to achieve satisfactory tracking performance. But modeling uncertainties, such as the errors are caused in the process of model simplification, always exist in the model-based controller design and degrade the control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large uncertainty of the system by learning ability to approximate arbitrary nonlinear functions and guarantee the closed-loop system is robust global asymptotic stabilization. In this paper, a novel vehicle dynamics stability control strategy is proposed using adaptive radial basis function network sliding mode control (ARBFN-SMC) theory to learn the system uncertainties and eliminate the effects of system uncertainties. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. The co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with integrated-electro-hydraulic brake system (I-EHB) actuator under a Sine With Dwell manoeuvre. The simulation results show that the ARBFN-SMC scheme has superior stability and tracking performances at different running conditions compared with the SMC scheme.

show abstract

Multi-objective stability control algorithm of heavy tractor semi-trailer based on differential braking

Cited by 39 publications

References 7 publications

Obstacle Avoidance of Semi-Trailers Based on Nonlinear Model Predictive Control

Obstacle Avoidance of Semi-Trailers Based on Nonlinear Model Predictive Control

Design and Validation of Differential Braking Controllers for Sport Utility Vehicles Considering the Interactions of Driver and Control System

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Contact Info

Product

Resources

About