Lane Tracking Control for Two-wheeled Vehicles by Considering Vehicle Dynamics

Abstract Although the vast majority of lane-tracking control methods rely on the steering wheel angle as the control input, a few studies have treated methods using the steering torque as the input. When operating vehicles especially at high speed, drivers typically do not grip the steering wheel tightly to prevent the angle of the steering wheel from veering off course. This study proposes a new steering assist system for a driver not with the steering angle but the steering torque as the input and clarifies the characteristics and relative advantages of the two approaches. Then using a newly developed driving simulator, characteristics of human drivers and the lane-tracking system based on the steering torque control are investigated.

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SET POINT TRACKING CONTROL OF A REMOTELY OPERATED VEHICLE USING MODEL PREDICTIVE CONTROL

The International Journal of Maritime Engineering ◽

10.5750/ijme.v162ia1.1124 ◽

2021 ◽

Vol 162 (A1) ◽

Author(s):

M P R Prasad

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Vehicle Dynamics ◽

Tracking Control ◽

Sliding Mode ◽

Pole Placement ◽

Control Technique ◽

Remotely Operated Vehicle ◽

Point Tracking ◽

Placement Technique

This paper considers kinematics and dynamics of Remotely Operated Underwater Vehicle (ROV) to control position, orientation and velocity of the vehicle. Cascade control technique has been applied in this paper. The pole placement technique is used in inner loop of kinematics to stabilize the vehicle motions. Model Predictive control is proposed and applied in outer loop of vehicle dynamics to maintain position and velocity trajectories of ROV. Simulation results carried out on ROV shows the good performance and stability are achieved by using MPC algorithm, whereas sliding mode control loses its stability when ocean currents are high. Implementation of proposed MPC algorithm and stabilization of vehicle motions is the main contribution in this paper.

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1103 Lane-tracking Control Based on an Animal Locomotion Model : an Applicaiton of Similarities between Bat Flight and Driver-operated Vehicle Trajectories

The Proceedings of the Transportation and Logistics Conference ◽

10.1299/jsmetld.2011.20.139 ◽

2011 ◽

Vol 2011.20 (0) ◽

pp. 139-142

Author(s):

Masateru AMANO ◽

Shoji ASAI ◽

Yoshikazu HATTORI ◽

Eiichi ONO

Keyword(s):

Tracking Control ◽

Animal Locomotion ◽

Lane Tracking ◽

Vehicle Trajectories

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Non-Traditional Controllers: Composite ABS/TCS for Wheeled Vehicle

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.711.491 ◽

2013 ◽

Vol 711 ◽

pp. 491-494

Author(s):

Ching Kuo Wang ◽

Chang Hsin Chang

Keyword(s):

Tracking Control ◽

Fuzzy Controller ◽

Viscous Friction ◽

Lumped Mass ◽

Braking System ◽

Wheeled Vehicle ◽

Nonlinear Features ◽

Braking Distance ◽

Rigid Body Motions ◽

Wheeled Vehicles

Modern vehicle dynamics in its broadest sense encompasses all forms of vehicles. It aims to improve the riding comfort and the maneuverability for high-quality automobiles. This paper develops a sensor-based fuzzy controller (SFC) with a composite anti-lock braking system and tracking control system (ABS/TCS) to navigate escaping motions of wheeled vehicles under the assumption of Coulombs viscous friction and lumped-mass/rigid-body motions. The so-called escaping dynamics of wheeled vehicles occurs when the vehicle escapes from the constrained space during braking or cornering. Traditionally, such slippage phenomenon is usually ignored because of its high frequency and strong nonlinear features. The proposed SFC is designed to shorten braking distance under emergent circumstances and minimize cornering radius to improve maneuverability for wheeled vehicles. Finally, detailed simulations of wheeled vehicles with a composite ABS/TCS under the assumption of Coulombs viscous friction are used to justify the SFC algorithm.

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Experiments of Urban Autonomous Navigation using Lane Tracking Control with Monocular Vision

Journal of Institute of Control Robotics and Systems ◽

10.5302/j.icros.2009.15.5.480 ◽

2009 ◽

Vol 15 (5) ◽

pp. 480-487 ◽

Cited By ~ 1

Keyword(s):

Tracking Control ◽

Autonomous Navigation ◽

Monocular Vision ◽

Lane Tracking

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Performance comparison of electric-vehicle drivetrain architectures from a vehicle dynamics perspective

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407019867491 ◽

2019 ◽

Vol 234 (4) ◽

pp. 915-935

Author(s):

Kerem Bayar

Keyword(s):

Electric Vehicle ◽

Control Strategy ◽

Vehicle Dynamics ◽

Tracking Control ◽

Tracking Error ◽

Vehicle Stability ◽

Electric Motors ◽

Sideslip Angle ◽

Stopping Distance ◽

Braking System

Recent electric vehicle studies in literature utilize electric motors within an anti-lock braking system, traction-control system, and/or vehicle-stability controller scheme. Electric motors are used as hub motors, on-board motors, or axle motors prior to the differential. This has led to the need for comparing these different drivetrain architectures with each other from a vehicle dynamics standpoint. With this background in place, using MATLAB simulations, these three drivetrain architectures are compared with each other in this study. In anti-lock braking system and vehicle-stability controller simulations, different control approaches are utilized to blend the electric motor torque with hydraulic brake torque; motor ABS, torque decomposition, and optimal slip-tracking control strategies. The results for the anti-lock braking system simulations can be summarized as follows: (1) Motor ABS strategy improves the stopping distance compared to the standard anti-lock braking system. (2) In case the motors are not solely capable of providing the required braking torque, torque decomposition strategy becomes a good solution. (3) Optimal slip-tracking control strategy improves the stopping distance remarkably compared to the standard anti-lock braking system, motor anti-lock braking system, and torque decomposition strategies for all architectures. The vehicle-stability controller simulation results can be summarized as follows: (1) higher affective wheel inertia of the on-board and hub motor architecture dictates a higher need of wheel torque in order to generate the tire force required for the desired yaw rate tracking. A higher level of torque causes a higher level of tire slip. (2) Optimal slip-tracking control strategy reduces the tire slip trends drastically and distributes the traction/braking action to each tire with the control-allocation algorithm specifying the reference slip values. This reduces reference tire slip-tracking error and reduces vehicle sideslip angle. (3) Tire slip trends are lower with the hub motor architecture, compared to the other architectures, due to more precise slip control.

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