Research on Path-Tracking Control Method of Intelligent Vehicle Based on Adaptive Two-Point Preview

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Yuan Guo ◽  
Tao Li ◽  
Long Huang ◽  
Zhiyuan Peng ◽  
Ming Ye ◽  
...  
Keyword(s):  
Tracking Control ◽  
Control Method ◽  
Path Tracking ◽  
Intelligent Vehicle

scholarly journals Path Tracking Control Method of a Modular Omnidirectional Vehicle.

2000 ◽  
Vol 66 (648) ◽  
pp. 2713-2720 ◽  
Author(s):  
Masafumi HASHIMOTO ◽  
Noriaki SUIZU ◽  
Fuminori OBA
Keyword(s):  
Tracking Control ◽  
Control Method ◽  
Path Tracking

scholarly journals Automatic Parking Path Planning and Tracking Control Research for Intelligent Vehicles

2020 ◽  
Vol 10 (24) ◽  
pp. 9100
Author(s):  
Chenxu Li ◽  
Haobin Jiang ◽  
Shidian Ma ◽  
Shaokang Jiang ◽  
Yue Li
Keyword(s):  
Path Planning ◽  
Tracking Control ◽  
Control Method ◽  
Sliding Mode ◽  
Vehicle Control ◽  
Path Tracking ◽  
Intelligent Vehicles ◽  
Parking System ◽  
Automatic Parking ◽  
Real Vehicle

As a key technology for intelligent vehicles, automatic parking is becoming increasingly popular in the area of research. Automatic parking technology is available for safe and quick parking operations without a driver, and improving the driving comfort while greatly reducing the probability of parking accidents. An automatic parking path planning and tracking control method is proposed in this paper to resolve the following issues presented in the existing automatic parking systems, that is, low degree of automation in vehicle control; lack of conformity between segmented path planning and real vehicle motion models; and low success rates of parking due to poor path tracking. To this end, this paper innovatively proposes preview correction which can be applied to parking path planning, and detects the curvature outliers in the parking path through the preview algorithm. In addition, it is also available for correction in advance to optimize the reasonable parking path. Meanwhile, the dual sliding mode variable structure control algorithm is used to formulate path tracking control strategies to improve the path tracking control effect and the vehicle control automation. Based on the above algorithm, an automatic parking system was developed and the real vehicle test was completed, thus exploring a highly intelligent automatic parking technology roadmap. This paper provides two key aspects of system solutions for an automatic parking system, i.e., parking path planning and path tracking control.

Digital robust preview control of path tracking

2005 ◽  
Vol 219 (1) ◽  
pp. 111-116
Author(s):  
Y G Tan ◽  
D K Liu ◽  
F Liu ◽  
Z D Zhou
Keyword(s):  
Control Systems ◽  
Tracking Control ◽  
Control Method ◽  
Path Tracking ◽  
Trajectory Control ◽  
Control Problems ◽  
Preview Control ◽  
Input Signals ◽  
Simulation Results ◽  
Reference Input

A robust optimal preview control method is presented in this paper for path tracking control problems to improve robustness and tracking precision of path tracking control systems. The known path information is used as reference input signals. Simulation results show that this method is valid not only for improving the performance of highly accurate trajectory control but also for improving system stabilization.

scholarly journals Pre-Work for the Birth of Driver-Less Scraper (LHD) in the Underground Mine: The Path Tracking Control Based on an LQR Controller and Algorithms Comparison

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7839
Author(s):  
Haoxuan Yu ◽  
Chenxi Zhao ◽  
Shuai Li ◽  
Zijian Wang ◽  
Yulin Zhang
Keyword(s):  
Tracking Control ◽  
Control Method ◽  
Clustering Algorithms ◽  
Development Trend ◽  
Path Tracking ◽  
Linear Quadratic ◽  
Trajectory Tracking Control ◽  
Linear Quadratic Optimal Control ◽  
The Future ◽  
Lqr Controller

With the depletion of surface resources, mining will develop toward the deep surface in the future, the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient mining of deep space is inseparable from movable and flexible production and transportation equipment such as scrapers. In the new era, intelligence is leading to the development trend of scraper (LHD), path tracking control is the key to the intelligent scraper (LHD), and it is also an urgent problem to be solved for unmanned driving. This paper describes the realization of the automatic operation of articulating the scraper (LHD) from two aspects, a mathematical model and trajectory tracking control method, and it focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, an LQR controller. On this basis, combined with different intelligent clustering algorithms, the parameters of the LQR controller are optimized to find the optimal solution of the LQR controller. Then, the path tracking control of an intelligent LHD unmanned driving technology is studied, focusing on the optimization of linear quadratic optimal control (LQR) and the intelligent cluster algorithms AGA, QPSO, and ACA; this research has great significance for the development of the intelligent scraper (LHD). As mining engineers, we not only need to conduct research for practical engineering projects but also need to produce theoretical designs for advanced mining technology; therefore, the area of intelligent mining is the one we need to explore at present and in the future. Finally, this paper serves as a guide to starting a conversation, and it has implications for the development and the future of underground transportation.

scholarly journals Research on LHD (Scraper) Path Tracking Control Based on LQR and Predict Pose Information

2021 ◽  
Author(s):  
Yulin Zhang ◽  
Chenxi Zhao ◽  
Zijian Wang ◽  
Haoxuan Yu
Keyword(s):  
Tracking Control ◽  
Control Method ◽  
Optimal Solution ◽  
Development Trend ◽  
Path Tracking ◽  
Trajectory Tracking Control ◽  
New Era ◽  
Transportation Equipment ◽  
Lqr Controller ◽  
Mining Work

With the depletion of shallow surface resources, the future mining work will develop towards the deep surface, and the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient exploitation of deep space cannot be separated from such mobile and flexible production and transportation equipment as scraper. In the new era, intelligence is the development trend of the LHD, and path tracking control technology is the key to the intelligent LHD, and it is also an urgent problem to be solved for its unmanned driving. This paper describes the realization of the automatic operation function of articulated LHD from two aspects of mathematical model and trajectory tracking control method, and focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, LQR controller. On this basis, the parameters of the LQR controller are optimized by combining different intelligent cluster algorithms to find the optimal solution of the LQR controller.

scholarly journals Model predictive control of steering torque in shared driving of autonomous vehicles

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042095013
Author(s):  
Chunjiang Bao ◽  
Jiwei Feng ◽  
Jian Wu ◽  
Shifu Liu ◽  
Guangfei Xu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Tracking Control ◽  
Control Method ◽  
Path Tracking ◽  
Hardware In The Loop ◽  
Control Loop ◽  
Vehicle Model ◽  
Automatic Driving ◽  
Steering Torque

The current path tracking control method is usually based on the steering wheel angle loop, which often makes the driver lose control of the automatic driving control loop. In order to involve the driver in the automatic driving control loop, and to solve the vehicle path tracking control problem with system robustness and model uncertainty, this paper puts forward a steering torque control method based on model predictive control algorithm. Based on the vehicle model, this method introduces the steering system model and the steering resistance torque model, and calculates the optimal control torque of the vehicle through the real-time vehicle status, so as to make up for the model mismatch, interference and other uncertainties, and ensure the real-time participation of the driver in the automatic driving control loop. To combine the nonlinear vehicle dynamics model with the steering column model, and to take the vehicle state parameters as the feedback variables of the model predictive controller model, then input the solution of the steering superposition control rate into the vehicle model, the design of the steering controller is realized. Finally, to carry out the simulation of lane keeping based on CarSim software and Simulink control model, and the hardware in-the-loop test on the hardware in-the-loop experimental platform of CarSim/LabVIEW-RT. The simulation and test results indicate that the designed torque loop path tracking control method based on model predictive control can help the driver track the target path better.

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