A Path Tracking Control Algorithm for Smart Wheelchairs

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.

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

Applied Sciences ◽

10.3390/app10186249 ◽

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.

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Parameter Self-Adjusting Path Tracking Algorithm of Mobile Robots

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.418.10 ◽

2013 ◽

Vol 418 ◽

pp. 10-14 ◽

Cited By ~ 3

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.

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A path tracking control algorithm for underwater mining vehicles

Journal of Mechanical Science and Technology ◽

10.1007/s12206-009-0436-y ◽

2009 ◽

Vol 23 (8) ◽

pp. 2030-2037 ◽

Cited By ~ 11

Author(s):

Sup Hong ◽

Jong-Su Choi ◽

Hyung-Woo Kim ◽

Moon-Cheol Won ◽

Seung-Chul Shin ◽

...

Keyword(s):

Tracking Control ◽

Control Algorithm ◽

Path Tracking ◽

Underwater Mining ◽

Mining Vehicles

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Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Sensors ◽

10.3390/s20154245 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4245

Author(s):

Keke Geng ◽

Nikolai Alexandrovich Chulin ◽

Ziwei Wang

Keyword(s):

Fault Detection ◽

Model Predictive Control ◽

Predictive Control ◽

Tracking Control ◽

Control Algorithm ◽

Fault Tolerant ◽

Autonomous Vehicle ◽

Path Tracking ◽

Fault Detection And Isolation ◽

Model Predictive Control Algorithm

The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

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