The Study on Co-Simulation Based Tracked Vehicle Path Tracking Control

2015 ◽  
Author(s):  
Can Wang ◽  
Bo Yang ◽  
Gangfeng Tan ◽  
YiRui Wang ◽  
Li Zhou
Keyword(s):  
Tracking Control ◽  
Path Tracking ◽  
Tracked Vehicle ◽  
Simulation Based ◽  
Vehicle Path

Autonomous vehicle path tracking control considering the stability under lane change

2021 ◽  
pp. 095965182199135
Author(s):  
Xiaolong Chen ◽  
Bing Zhou ◽  
Xiaojian Wu
Keyword(s):  
Tracking Control ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Linear Time ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Steering Angle ◽  
Adhesion Coefficient ◽  
The Road ◽  
Vehicle Path

Considering that when a vehicle travels on a low friction coefficient road with high speed, the path tracking ability declines. To keep the performance of path tracking and improve the stabilization under that situation, this article presents approaches to estimate the parameters and control the vehicle. First, the key states of the vehicle and the road adhesion coefficient are estimated by the unscented Kalman filter. This is followed by applying the linear time-varying model-based predictive controller to achieve path tracking control, and the initial tire steering angle control rate is obtained. Finally, the steering angle compensation controller is simultaneously designed by a simple receding horizon corrector algorithm to improve vehicle stability when the path is tracked on a low-adhesion coefficient or at high speed. The performance of the proposed approach is evaluated by software CarSim and MATLAB/Simulink. Simulation results show that an improvement in the performance of path tracking and stabilization can be achieved by the integrated controller under the variable road adhesion coefficient condition and high speed with 110 km/h.

Development of path tracking control of a tracked vehicle for an unmanned ground vehicle

2020 ◽  
Vol 8 (4) ◽  
pp. 136
Author(s):  
Muhammad Akhimullah Subari ◽  
Khisbullah Hudha ◽  
Zulkiffli Abd Kadir ◽  
Syed Mohd Fairuz Bin Syed Mohd Dardin ◽  
Noor Hafizah Amer
Keyword(s):  
Tracking Control ◽  
Path Tracking ◽  
Unmanned Ground Vehicle ◽  
Tracked Vehicle ◽  
Ground Vehicle

Research on autonomous vehicle path tracking control considering roll stability

2020 ◽  
Vol 235 (1) ◽  
pp. 199-210
Author(s):  
Fen Lin ◽  
Shaobo Wang ◽  
Youqun Zhao ◽  
Yizhang Cai
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
Tracking Control ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Vehicle Path ◽  
Target Path

For autonomous vehicle path tracking control, the general path tracking controllers usually only consider vehicle dynamics’ constraints, without taking vehicle stability evaluation index into account. In this paper, a linear three-degree-of-freedom vehicle dynamics model is used as a predictive model. A comprehensive control method combining Model Predictive Control and Fuzzy proportional–integral–derivative control is proposed. Model Predictive Control is used to control the vehicle yaw stability and track the target path by considering the front wheel angle, sideslip angle, tire slip angles, and yaw rate during the path tracking. Fuzzy proportional–integral–derivative algorithm is adopted to maintain the vehicle roll stability by controlling the braking force of each tire. Co-simulation with CarSim and MATLAB/Simulink shows the designed controller has good tracking performance. The controller is smooth and effective and ensures handling stability in tracking the target path.

Path tracking control of an autonomous vehicle with model-free adaptive dynamic programming and RBF neural network disturbance compensation

2021 ◽  
pp. 095440702110338
Author(s):  
Hongbo Wang ◽  
Chenglei Hu ◽  
Juntao Zhou ◽  
Lizhao Feng ◽  
Bin Ye ◽  
...  
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Tracking Control ◽  
Autonomous Vehicle ◽  
Front Wheel ◽  
Path Tracking ◽  
Adaptive Dynamic Programming ◽  
Model Parameters ◽  
Model Free ◽  
Vehicle Path

The performance of the model-based controller is always affected by the uncertainty and nonlinearity of the model parameters in the vehicle path tracking process. To address this issue, a novel path tracking controller based on model-free adaptive dynamic programming (ADP) is proposed for autonomous vehicles in this paper. To be specific, the proposed controller obtains information from the online state and front-wheel angle input data which are repeatedly used to calculate the controller gain iteratively. So, this controller features not requiring accurate knowledge of vehicle model parameters for controller development. Meanwhile, the path tracking performance of the autonomous vehicle will be inevitably disturbed by unknown nonlinear external disturbance. To approximate this disturbance, the learning characteristics of Radial Basis Function Neural Network (RBFNN) are applied to generate compensation for the front-wheel angle. Afterward, the weight updating law of RBFNN is derived by Lyapunov function to ensure the stability and convergence of the whole system. Finally, Hardware in the loop (HIL) test results demonstrate that the proposed ADP-RBF controller can improve the comprehensive performance of the vehicle path tracking control system and achieve the balance between path tracking accuracy and minimum sideslip angle.

Development of path tracking control of a tracked vehicle for an unmanned ground vehicle

2020 ◽  
Vol 8 (4) ◽  
pp. 136
Author(s):  
Noor Hafizah Amer ◽  
Syed Mohd Fairuz Bin Syed Mohd Dardin ◽  
Khisbullah Hudha ◽  
Zulkiffli Abd Kadir ◽  
Muhammad Akhimullah Subari
Keyword(s):  
Tracking Control ◽  
Path Tracking ◽  
Unmanned Ground Vehicle ◽  
Tracked Vehicle ◽  
Ground Vehicle
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