## 1 Introduction  
The four-wheel drive, braking force and steering angle can be independently controlled with multi-degrees of freedom, and the full linear control distributed-drive electric automobile has become the new energy intelligent electric vehicle research hot spot, but it may bring the risk of actuator failure. When some of the actuators fail, the reasonable control of the fully line-controlled distributed-drive electric vehicle to guarantee the precision, stability and safety of traceability has become a key topic of research. The technology of trajectory tracking control is critical for the realization of unmanned fully line-controlled distributed drive electric vehicles. With the speedy development of unmanned fully line-controlled distributed drive electric vehicles' technology, the questions of accuracy, stability as well as the safety of trajectory tracking control have gained wide attention. In this area, numerous investigations have been carried out in various countries, including pure pursuit [1, 2], Stanley [3], sliding mode [4, 5], adaptive [6, 7], model predictive [8, 9], nonlinear model predictive control [10], potential field control [11, 12, 13], and feed-forward pre-scanning control [14]. [PERSON] et al. conceived of a comprehensive control architecture for path planning and tracking control by MPC theory to enhance the security and trajectory following precision of the autonomous car [15]. [PERSON] applied a sliding mode variable construction control technique to provide fast and stable trajectory tracking by estimating the lateral oscillation angular velocity. [16]. [PERSON] et al. introduced an LPV technique for automatic route-following in the face of steering drive nonlinearity, which successfully reduced tracking errors [17]. [PERSON] et al. designed adaptive time-domain parameter algorithms for trajectory tracking controllers, and simulation experiments showed that they improved the maneuvering stability, trajectory tracking accuracy and safety performance of self-driving vehicles [18]. An EMPC with a flexible artificial potential field was created by [PERSON] et al. which implemented the automatic electric vehicle to avoid obstacles and trajectory tracking, and experimental results indicated that the designed controller enhanced the effectiveness of obstacle avoidance and track following and ensured the system reliability [19]. [PERSON] et al. investigated the trajectory of an independent four-wheel drive steering system's tracking control machine at high speeds and proposed a feedforward, feedback and a dynamic constraint-based MPC algorithm to improve high-speed trajectory tracking performance [20]. [PERSON] et al. conducted a study on track following control for a driverless-type wheeled tractor using an upgraded quantum genetic LQR method. The results of the simulation demonstrated that the tractor has excellent tracking precision as well as stability [21]. [PERSON] et al. employed MPC and fuzzy PID-based controllers for intelligent driving vehicle trajectory-tracking control, and the simulated results suggest that the controller has outstanding behavior [22]. [PERSON] et al. coupled vertical and horizontal control of the driverless car using an offset-free MPC algorithm to realize improved trajectory-following precision and high-speed reliability [23]. [PERSON] et al. proposed a two-level controller for horizontal trail-following control for self-driving cars, using an upper level continuous that changed over time, an MPC controller and a function with a radial basis proportional-integral derivation neural network controller in the lower level. The calculation showed that the designed controller can ensure car reliability and good trajectory-following accuracy [24]. [PERSON] et al. presented a track tracking control algorithm for the smart electric automobile according to the adaptive optimal control of weight coefficients, which effectively reduced tracking errors through experimental verification [25].