## 1 Introduction
A double neural internetwork active fault control strategy based on the feedforward offset technique of neural network estimation was proposed by [PERSON] et al. Both emulation and experimentation leads prove that the control strategy has good capability [26]. [PERSON] et al. proposed a longitudinal and lateral force co-reconfiguration fault-tolerant controllers policy according to active turn control, which was shown to significantly improve vehicle reliability of driving and security in the event of an actuator failure [27]. Based on deep reinforcement learning theory, [PERSON] suggested a fault-tolerant control system for four-wheel drive electric cars. Vehicle stability, driver comfort and power consumption were effectively improved [28]. [PERSON], [PERSON] et al. designed an improved dynamic Kalman filter and proposed a model predictive control method considering the anti-tilt constraint. The analogue findings indicate the approach's utility [29]. [PERSON] proposed a hierarchical plan and follow-up frame for self-driving cars. The simulation results show the ability to solve most common scenarios on structured roads with reasonable decision and control capabilities [30]. One of the trends in the development of intelligent vehicles today is to equip them with more sensors, particularly in the case of electric vehicles. These sensors provide a wealth of data and information that can be used for vehicle control and driver assistance systems. However, ensuring synchronization of different sensors in terms of time and space is crucial for accurate vehicle control signal input. There are ongoing research efforts to address this issue. For example, [31] is a study that uses deep learning techniques to detect tasks in crops using data from multiple sensors and transfer learning to improve detection accuracy. To conclude, there currently exist a number of established methods for track-following management of typical low-speed circumstances, but there are numerous crises in complex traffic scenarios, such as wire-controlled steering system failure, etc. The proposed trajectory tracking-control strategy addresses the problem of ensuring track-following precision and reliability in the track-following control process of unmanned cars, while fully considering the impact of a malfunctioning wire-controlled turning system on vehicle trajectory-tracking driving, designing wire-controlled steering fault-tolerant techniques and the validity and robustness of the strategies are also validated through experimentation. In this article, we examine the reasonable control of the braking, driving, steering and fault-tolerant systems when some actuators fail to achieve the lability of track-following precision security. The complete vehicle control strategy for trajectory tracking control with fault-tolerant control of an unmanned fully line-controlled distributed drive electric vehicle is displayed in Figure 1.  
As shown in Figure 1, \(\dot{X}\) for vertical velocity, \(\dot{Y}\) for horizontal velocity, \(X\) for vertical displacement, \(Y\) for horizontal displacement, \(\varphi\) is yaw, \(\dot{\varphi}\) is yaw rate. The whole vehicle is controlled in layers, with a signal processing layer, control layer and executive layer. Signal processing layer: according to the collected information on the exact status of the vehicle at a certain time by sensors and the planned reference route for the variances between the two sources of information, send the results to the control layer. The fault diagnosis module also detects the status of the steering-by-wire sensors and actuators and sends the results to the control layer. The thesis focuses on the trajectory tracking control and fault-tolerant control of an unmanned fully line-controlled distributed drive electric vehicle, so the track-following control at the control level consists mainly of transverse motion control and vertical motion control.