Integrated Vehicle Controller for Path Tracking with Rollover Prevention of Autonomous Articulated Electric Vehicle Based on Model Predictive Control

被引:9
作者
Jeong, Yonghwan [1 ]
机构
[1] Seoul Natl Univ Sci & Technol, Dept Mech & Automot Engn, 232 Gongneung Ro, Seoul 01811, South Korea
关键词
autonomous articulated electric vehicle; path-tracking control; velocity control; rollover prevention; model predictive control; STABILITY CONTROL; ELECTRIFICATION; DESIGN;
D O I
10.3390/act12010041
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This paper presents an integrated controller for an autonomous articulated electric vehicle (AAEV) for path tracking and rollover prevention. The AAEV is vulnerable to rollover due to the characteristics of the articulated frame steering (AFS) mechanism, which shows improved maneuverability and agility but not front wheel steering. In addition, the ratio between height and track width is high, so the AAEV is prone to rolling over. Therefore, the proposed controller was designed to achieve the two goals, following the reference path and managing the velocity to improve the safety of the AAEV. Vehicle behavior was modeled by a kinematic model with actuation delay. A local linearization was used to improve the accuracy of the vehicle model and reduce the computational load. Reference states of the position and heading were determined to follow the reference path and prevent the rollover. A model predictive control (MPC)-based reference state tracker was designed to optimize the articulation angle rate and longitudinal acceleration commands. The simulation study was conducted to evaluate the proposed algorithm with a comparison of the base algorithms. The reference path for the simulation was an S-shaped path with discontinuous curvature. Simulation results showed that the proposed algorithm reduces the path tracking error and load-transfer ratio.
引用
收藏
页数:27
相关论文
共 56 条
[1]   CasADi: a software framework for nonlinear optimization and optimal control [J].
Andersson, Joel A. E. ;
Gillis, Joris ;
Horn, Greg ;
Rawlings, James B. ;
Diehl, Moritz .
MATHEMATICAL PROGRAMMING COMPUTATION, 2019, 11 (01) :1-36
[2]  
Azad N. L., 2005, International Journal of Vehicle Systems Modelling and Testing, V1, P106, DOI 10.1504/IJVSMT.2005.008575
[3]   Self-driving cars: A survey [J].
Badue, Claudine ;
Guidolini, Ranik ;
Carneiro, Raphael Vivacqua ;
Azevedo, Pedro ;
Cardoso, Vinicius B. ;
Forechi, Avelino ;
Jesus, Luan ;
Berriel, Rodrigo ;
Paixao, Thiago M. ;
Mutz, Filipe ;
Veronese, Lucas de Paula ;
Oliveira-Santos, Thiago ;
De Souza, Alberto F. .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 165
[4]   A New Path Tracking Method Based on Multilayer Model Predictive Control [J].
Bai, Guoxing ;
Meng, Yu ;
Liu, Li ;
Luo, Weidong ;
Gu, Qing ;
Li, Kailun .
APPLIED SCIENCES-BASEL, 2019, 9 (13)
[5]   Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control [J].
Bai, Guoxing ;
Liu, Li ;
Meng, Yu ;
Luo, Weidong ;
Gu, Qing ;
Ma, Baoquan .
APPLIED SCIENCES-BASEL, 2019, 9 (07)
[6]  
Bao J.-H., 2011, P 2011 INT C EL INF
[7]   Vehicle Electrification: Status and Issues [J].
Boulanger, Albert G. ;
Chu, Andrew C. ;
Maxx, Suzanne ;
Waltz, David L. .
PROCEEDINGS OF THE IEEE, 2011, 99 (06) :1116-1138
[8]   Electrification of roads: Opportunities and challenges [J].
Chen, Feng ;
Taylor, Nathaniel ;
Kringos, Nicole .
APPLIED ENERGY, 2015, 150 :109-119
[9]  
Coulter R.C., 1992, ADA255524 UNIV INST
[10]   Backstepping control for lateral guidance of all-wheel steered multiple articulated vehicles [J].
de Bruin, D ;
Damen, AAH ;
Pogromsky, A ;
van den Bosch, PPJ .
2000 IEEE INTELLIGENT TRANSPORTATION SYSTEMS PROCEEDINGS, 2000, :95-100