Parameter Self-Adjusting Path Tracking Algorithm of Mobile Robots

2013 ◽  
Vol 418 ◽  
pp. 10-14 ◽  
Author(s):  
Hong Ji Zhang ◽  
Yuan Yuan Ge
Keyword(s):  
Tracking Control ◽  
Control Algorithm ◽  
Control Object ◽  
Learning Objects ◽  
Path Tracking ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Path Tracking Algorithm ◽  
Robot Path

For conventional fuzzy path tracking controller need to manually updated the control parameters in order to get better tracking control deficiencies and the lack of robustness of the problem when the control object is disturbed. Parameters self-adjusting tracking algorithm is proposed based on Cerebellum Model Articulation Controller(CMAC) and fuzzy logic composite of the control. The CAMC control logarithm first charged with tracking through learning objects charged with approximation of the object model, to learning cycle worth to control corresponding to the amount of correction corresponding weight value according to the error between input and output of the system and set the learning rate. When the object or environment changes can make the control performance of the system is automatically adjusting within a certain range, since the role of the CAMC. Tracking experiments show that. The tracking control algorithm has high tracking accuracy and good robustness, is conducive to the overall optimization of robot path tracking.

Research and Experiment on Path-Tracking Control of Autonomous Tractor Based on Lateral Deviation and Yaw Rate Feedback

2021 ◽  
Vol 37 (5) ◽  
pp. 891-899
Author(s):  
Bingli Zhang ◽  
Jin Cheng ◽  
Pingping Zheng ◽  
Aojia Li ◽  
Xiaoyu Cheng
Keyword(s):  
Tracking Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Path Tracking ◽  
Steering Angle ◽  
Lateral Deviation ◽  
Yaw Rate ◽  
Automatic Navigation ◽  
Rate Feedback ◽  
The Ideal

HighlightsAutomatic navigation technology in autonomous tractors is one of the key technologies in precision agriculture.A path-tracking control algorithm based on lateral deviation and yaw rate feedback is proposed.The modified steering angle was obtained by comparing the ideal yaw rate with the actual yaw rate.The results demonstrate the efficiency and superior accuracy of the proposed algorithm for tractor path-tracking control.Abstract. The performance of path-tracking control systems for autonomous tractors affects the quality and efficiency of farmland operations. The objective of this study was to develop a path-tracking control algorithm based on lateral deviation and yaw rate feedback. The autonomous tractor path lateral dynamics model was developed based on preview theory and a two-degree-of-freedom tractor model. According to the established dynamic model, a path-tracking control algorithm using yaw angular velocity correction was designed, and the ideal steering angle was obtained by lateral deviation and sliding mode control. The modified steering angle was obtained by a proportional-integral-derivative feedback controller after comparing the ideal yaw rate with the actual yaw rate, which was then combined with the ideal steering angle to obtain the desired steering angle. The simulation and experimental results demonstrate the efficiency and superior accuracy of the proposed tractor path-tracking control algorithm, enabling its application in automatic navigation control systems for autonomous tractors. Keywords: Autonomous tractor, Path-tracking control, Sliding mode control, Yaw rate feedback.

scholarly journals INTELLIGENT CONTROL TECHNOLOGY OF AGRICULTURAL GREENHOUSE OPERATION ROBOT BASED ON FUZZY PID PATH TRACKING ALGORITHM

2020 ◽  
pp. 181-190
Author(s):  
Ren Qun
Keyword(s):  
Control System ◽  
Pid Control ◽  
Fuzzy Controller ◽  
Good Control ◽  
Path Tracking ◽  
Tracking Algorithm ◽  
Practical Significance ◽  
Fuzzy Pid ◽  
Path Tracking Algorithm

With the development of agricultural automation, applying intelligent algorithms to the navigation control of agricultural work vehicles has important practical significance for improving vehicle navigation accuracy and operation efficiency. In view of the complexity of the agricultural greenhouse environment, this study proposed a fuzzy PID path tracking algorithm based on the traditional vehicle PID control system. This algorithm uses a fuzzy controller to improve the PID control system, thereby realizing the online setting of PID control parameters. In order to verify the effectiveness of the fuzzy PID path tracking algorithm, the improved control system was applied to the tracked vehicle robot of Beijing Forestry University, and the operation performance of the vehicle robot was tested. The research results show that the absolute error rate of vehicle robot distance measurement is less than 1%; the error of the man-machine follow-up test is between 4 and 7 cm, and the measured follow-up distance is slightly less than the safe follow-up distance; the maximum error of the vehicle's fixed-point parking is 0.3 cm; The linear position tracking control has a lateral position deviation of ±3cm, and the vehicle's linear driving control and steering effects are better. The fuzzy PID path tracking algorithm designed this time shows good control performance, which has reference significance for the practical application of agricultural robots.

scholarly journals A Robust Numerical Path Tracking Algorithm for Polynomial Homotopy Continuation

2020 ◽  
Vol 42 (6) ◽  
pp. A3610-A3637
Author(s):  
Simon Telen ◽  
Marc Van Barel ◽  
Jan Verschelde
Keyword(s):  
Path Tracking ◽  
Tracking Algorithm ◽  
Homotopy Continuation ◽  
Path Tracking Algorithm

scholarly journals Robust Path Tracking Control for Autonomous Vehicle Based on a Novel Fault Tolerant Adaptive Model Predictive Control Algorithm

2020 ◽  
Vol 10 (18) ◽  
pp. 6249
Author(s):  
Keke Geng ◽  
Shuaipeng Liu
Keyword(s):  
Predictive Control ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
Control Algorithm ◽  
Fault Tolerant ◽  
Path Tracking ◽  
Adaptive Model ◽  
Chi Square ◽  
Model Predictive Control Algorithm ◽  
Adaptive Model Predictive Control

Autonomous vehicles are expected to completely change the development model of the transportation industry and bring great convenience to our lives. Autonomous vehicles need to constantly obtain the motion status information with on-board sensors in order to formulate reasonable motion control strategies. Therefore, abnormal sensor readings or vehicle sensor failures can cause devastating consequences and can lead to fatal vehicle accidents. Hence, research on the fault tolerant control method is critical for autonomous vehicles. In this paper, we develop a robust fault tolerant path tracking control algorithm through combining the adaptive model predictive control algorithm for lateral path tracking control, improved weight assignment method for multi-sensor data fusion and fault isolation, and novel federal Kalman filtering approach with two states chi-square detector and residual chi-square detector for detection and identification of sensor fault in autonomous vehicles. Our numerical simulation and experiment demonstrate that the developed approach can detect fault signals and identify their sources with high accuracy and sensitivity. In the double line change path tracking control experiment, when the sensors failure occurs, the proposed method shows better robustness and effectiveness than the traditional methods. It is foreseeable that this research will contribute to the development of safer and more intelligent autonomous driving system, which in turn will promote the industrial development of intelligent transportation system.

scholarly journals A Path Tracking Method for Autonomous Rice Drill Seeder in Paddy Fields

2018 ◽  
Vol 220 ◽  
pp. 04004
Author(s):  
Yan Zhang ◽  
Yanming Li ◽  
Yixiang Huang ◽  
Xiangpeng Liu ◽  
Chengliang Liu
Keyword(s):  
Control Model ◽  
Path Tracking ◽  
Tracking Algorithm ◽  
Paddy Fields ◽  
Steering Control ◽  
Lateral Deviation ◽  
Angle Deviation ◽  
Heading Angle ◽  
The Mean ◽  
Path Tracking Algorithm

For the purpose of overcoming the obstacles in application of autonomous rice drill seeder in paddy fields, a path tracking algorithm with high accuracy used for steering control during straight traveling in uneven muddy paddy fields is introduced in this paper. Combining lateral deviation and heading angle deviation as feedback, a nonlinear steering control model is developed in the algorithm. To avoid the position error caused by incline, the influence brought by the roll angle and the pitch angle of the vehicle on position coordinates are taken into account when the vehicle is on a slant. The experiments carried out in arable paddy fields show that the mean absolute lateral deviation of the algorithm was less than 2.9 centimeters and the heading angle deviation was less than 0.03°. The path tracking algorithm is able to meet the required precision for autonomous rice drill seeder in paddy fields of China.

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