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

Abstract

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.

Research on intelligent vehicle path tracking control based on preview distance optimization

“…From the literature [27], it is known that the width of the second, third and fourth class roads are all 3.5m, and the width of this vehicle is 2m. Assuming that the vehicle drives along the centerline, the limit distance between the vehicle and both sides of the road is 0.75m, as shown in Figure 6.…”

Optimal Control Method of Path Tracking for Four-Wheel Steering Vehicles

“…Geometry-based control methods are often used in low-speed scenarios, with good interpretability and fast calculation speed. Model-based methods mainly based on dynamic models are often used for stability control of high-speed vehicles [11], whose disadvantages include poor real-time performance and the difficulty to obtain kinetic parameters accurately [12]. However, the above studies are mostly based on front-wheel steering (FWS) vehicles.…”

Trends in Sensors Fault Diagnosis

“…Closer examination of [ 6 ], as well as [ 7 ], may bring the reader to a conclusion that especially linear models are worth studying. In the case of [ 6 ], this is a simple, but powerful linearization around an operating point, whereas in [ 7 ], the authors propose a Takagi–Sugeno fuzzy model composed of a number of linear sub-models. While similar in modeling approaches, both papers deal with fault diagnosis in a slightly different way.…”

“…Some analytical model-based approaches are also in this area of interest. Closer examination of [6], as well as [7], may bring the reader to a conclusion that especially linear models are worth studying. In the case of [6], this is a simple, but powerful linearization around an operating point, whereas in [7], the authors propose a Takagi-Sugeno fuzzy model composed of a number of linear sub-models.…”