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

Sensors ◽  
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.

scholarly journals Research on Tracking Control of A Quadrotor UAV Based on Model Predictive Control and PD Control

2021 ◽  
Vol 2085 (1) ◽  
pp. 012008
Author(s):  
Jimin Yu ◽  
Zhi Yong ◽  
Yousi Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Tracking Control ◽  
Control Algorithm ◽  
Control Method ◽  
Control Input ◽  
Pd Control ◽  
Quadrotor Uav ◽  
Model Predictive Control Algorithm ◽  
Method Model

Abstract In order to solve the problem of path tracking of a quadrotor UAV, this paper proposes a track tracking control method which combines Model Predictive Control algorithm and PD control method. Model Predictive Control algorithm can generate control input for formation flight and track the specified trajectory. PD control can achieve rapid response to attitude and adjust error quickly. The simulation results verify the effectiveness of the proposed control method.

scholarly journals Optimal Speed Plan for the Overtaking of Autonomous Vehicles on Two-Lane Highways

2020 ◽  
Vol 5 (5) ◽  
pp. 44 ◽  
Author(s):  
Said M. Easa ◽  
Maksym Diachuk
Keyword(s):  
Predictive Control ◽  
Autonomous Vehicles ◽  
Optimization Technique ◽  
Vehicle Speed ◽  
Adaptive Model ◽  
The Finite Element Method ◽  
Speed Distribution ◽  
Rational Distribution ◽  
Adaptive Model Predictive Control ◽  
Speed Mode

In passing maneuvers on two-lane highways, assessing the needed distance and the potential power reserve to ensure the required speed mode of the passing vehicle is a critical task of speed planning. This task must meet several mutually exclusive conditions that lead to successful maneuvers. This paper addresses three main aspects. First, the issues associated with a rational distribution of the speed of the passing vehicle for overtaking a long commercial vehicle on two-lane highways are discussed. The factors that affect the maneuver effectiveness are analyzed, considering the safety and cost. Second, a heuristic algorithm is proposed based on the rationale for choosing the necessary space and time for overtaking. The initial prediction’s sensitivity to fluctuations of the current measurements of the position and speed of the overtaking participants is examined. Third, an optimization technique for the passing vehicle speed distribution during the overtaking time using the finite element method is presented. Adaptive model predictive control is applied for tracking the references being generated. The presented model is illustrated using a simulation.

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