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Article

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

1
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
2
School of Automation Systems, Moscow Bauman State Technical University, Moscow 109807, Russia
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4245; https://doi.org/10.3390/s20154245
Received: 29 June 2020 / Revised: 25 July 2020 / Accepted: 28 July 2020 / Published: 30 July 2020
(This article belongs to the Special Issue Sensors Fault Diagnosis Trends and Applications)
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. View Full-Text
Keywords: autonomous vehicle; model predictive control; path tracking control; fault detection and isolation autonomous vehicle; model predictive control; path tracking control; fault detection and isolation
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MDPI and ACS Style

Geng, K.; Chulin, N.A.; Wang, Z. Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle. Sensors 2020, 20, 4245. https://doi.org/10.3390/s20154245

AMA Style

Geng K, Chulin NA, Wang Z. Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle. Sensors. 2020; 20(15):4245. https://doi.org/10.3390/s20154245

Chicago/Turabian Style

Geng, Keke, Nikolai A. Chulin, and Ziwei Wang. 2020. "Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle" Sensors 20, no. 15: 4245. https://doi.org/10.3390/s20154245

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