Implementation and Development of a Trajectory Tracking Control System for Intelligent Vehicle

2018 ◽  
Vol 94 (1) ◽  
pp. 251-264 ◽  
Author(s):  
Junyu Cai ◽  
Haobin Jiang ◽  
Long Chen ◽  
Jun Liu ◽  
Yingfeng Cai ◽  
...  
Keyword(s):  
Control System ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Intelligent Vehicle ◽  
Trajectory Tracking Control

scholarly journals Expected yaw rate–based trajectory tracking control with vision delay for intelligent vehicle

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042093427
Author(s):  
Qiu Xia ◽  
Long Chen ◽  
Xing Xu ◽  
Yingfeng Cai ◽  
Haobin Jiang ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Intelligent Vehicle ◽  
Automatic Identification ◽  
Identification System ◽  
Vehicle Stability ◽  
Trajectory Tracking Control ◽  
Automatic Identification System ◽  
Kalman Predictor ◽  
Simulation Results

Accurate and real-time position of preview point is significant to trajectory tracking control of vision-guided intelligent vehicle. The unavoidable delay of road automatic identification system weakens trajectory tracking control performance, and even deteriorates the vehicle stability. Therefore, a compensator for the delay of road automatic identification system was proposed which combines the current statistical model and adaptive Kalman predictor to estimate the state of preview point position. The trajectory tracking sliding mode controller of intelligent vehicle is established through a 2–degrees of freedom vehicle dynamic model and motion model by using MATLAB/Simulink and CarSim. The trajectory tracking performance under 20–100 ms delay is analyzed. The simulation results show that the trajectory tracking performance of intelligent vehicle will be affected by the delay of road automatic identification system, reducing tracking accuracy. And when the delay is too large, it will deteriorate the vehicle stability and safety. In addition, the simulation results also verify the effectiveness of current statistical–adaptive Kalman predictor compensator at different delays.

Trajectory tracking control system for ship

2004 ◽  
Vol 37 (10) ◽  
pp. 251-255 ◽  
Author(s):  
Janusz Pomirski ◽  
Leszek Morawski ◽  
Andrzej Rak
Keyword(s):  
Control System ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Trajectory Tracking Control

Theoretical Analysis and Experimental Study on Automatic Trajectory Tracking Control of a Shield Tunneling Machine under Pressure Regulation Working Mode

2011 ◽  
Vol 317-319 ◽  
pp. 1444-1451
Author(s):  
Hai Bo Xie ◽  
Xiao Ming Duan ◽  
Hua Yong Yang ◽  
Zhi Bin Liu
Keyword(s):  
Control System ◽  
Working Conditions ◽  
Trajectory Tracking ◽  
Pid Control ◽  
Tracking Control ◽  
Shield Tunneling ◽  
Trajectory Tracking Control ◽  
Variable Gain ◽  
Thrust System ◽  
Tunneling Machine

Hydraulic thrust system is a critical part of shield tunneling machine. Automatic trajectory tracking control is a significant task of thrust system during tunnel excavation. In this article, plane mechanical structure diagram of the thrust system and path planning method are illustrated at first. An integrated control system is proposed to achieve the automatic control of the thrust trajectory. The control system consists of one trajectory planning controller for both cylinders and an individual cylinder controller for each of hydraulic cylinders. Trajectory planning controller is used to generate respective displacement signals of double-cylinder in every thrust stroke and each of cylinder controllers is used to realize the precise control of the given thrust trajectory. Variable-gain PID control strategy applied to achieve the precise tracking control of thrust trajectory under several typical working conditions are done at last. The experimental results demonstrate that variable-gain PID control have good performances with short response time and small overshoot regardless of changes of working conditions.

Non-Autonomous Wheeled Mobile Robot Trajectory Tracking Control Based on the Navigation via WSN

2012 ◽  
Vol 457-458 ◽  
pp. 1089-1095
Author(s):  
Da Yong Lu ◽  
Zhen Hua Luo ◽  
Jian Lu Tian
Keyword(s):  
Control System ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Base Station ◽  
Reference Trajectory ◽  
Distributed Network ◽  
Feedback Channel ◽  
Wheeled Mobile Robots ◽  
Trajectory Tracking Control ◽  
Robot Trajectory

Trajectory tracking control is a major control problem in the application of the wheeled mobile robots (WMRs). However, many of the WMRs are autonomous which are equipped with several kinds of sensor to detect the position and orientation by itself, such as ultrasonic, laser, infrared and visual. This paper studies the implementation for a distributed network feedback control system and the trajectory tracking control algorithm for a non-autonomous WMR. The control system consists of a wireless sensor network (WSN) and a monitoring base station for measurement and feedback, and a non-autonomous WMR as controlled plant. To cope with the distributed and asynchronous measurements and slow response of the feedback channel, the sectional error amplitude limited algorithm is studied and the results show the system can effectively track a reference trajectory.

The Spherical Rolling-Flying Vehicle: Dynamic Modeling and Control System Design

2021 ◽  
pp. 1-23
Author(s):  
Stefan Atay ◽  
Matthew Bryant ◽  
Gregory D. Buckner
Keyword(s):  
Control System ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
High Mobility ◽  
Theoretical Development ◽  
Trajectory Tracking Control ◽  
Modeling And Control ◽  
And Control

Abstract This paper presents the dynamic modeling and control of a bi-modal, multirotor vehicle that is capable of omnidirectional terrestrial rolling and multirotor flight. It focuses on the theoretical development of a terrestrial dynamic model and control systems, with experimental validation. The vehicle under consideration may roll along the ground to conserve power and extend endurance but may also fly to provide high mobility and maneuverability when necessary. The vehicle employs a three-axis gimbal system that decouples the rotor orientation from the vehicle's terrestrial rolling motion. A dynamic model of the vehicle's terrestrial motion is derived from first principles. The dynamic model becomes the basis for a nonlinear trajectory tracking control system suited to the architecture of the vehicle. The vehicle is over-actuated while rolling, and the additional degrees of actuation can be used to accomplish auxiliary objectives, such as power optimization and gimbal lock avoidance. Experiments with a hardware vehicle demonstrate the efficacy of the trajectory tracking control system.

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