# Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network
## 2 Literature Review  
An effective tracking control scheme is critical to UGV path tracking control. Because UGV path tracking control is a strongly nonlinear process, the control methods for previous linear systems seem to be inadequate [25]. At present, the common control methods include adaptive control, sliding mode control, fuzzy control, neural network control, and predictive control [26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. For instance, for formation control of multiple vehicles, the leader-follower error model was built and an adaptive controller was designed to address the uncertain relative distance using the dynamic estimation of the leader-follower distance [26]. A multivariable model reference adaptive control (MRAC) scheme was studied for the automatic carrier-landing control problem of unmanned aerial vehicles with the system dynamics of nonlinearity, multivariable coupling, and parametric uncertainty [27]. A vehicle lateral controller was designed for autonomous vehicles based on higher-order sliding mode control [28]. An automatic steering control strategy for unmanned vehicles based on robust backstepping sliding mode control theory was proposed [29]. For the accuracy and stability of driverless buses, a path-tracking controller based on fuzzy pure pursuit control with a front axle reference (FPPC-FAR) was proposed [30]. An obstacle avoidance strategy consisting of path planning and robust fuzzy output feedback control was proposed for unmanned ground vehicles [31]. A novel adaptive neural network robust lateral motion control method was presented that can maintain the yaw stability of an autonomous vehicle while minimizing the lateral path tracking error at the limits of driving conditions [32]. A learning-based predictive control approach was presented for an autonomous racing car [33]. A nonlinear model predictive control optimization algorithm based on the collocation method was proposed for unmanned vehicle trajectory tracking [34]. A trajectory tracking error-based model was used to design a linear model predictive controller and its control action was combined with feed forward and robust control actions [35]. However, all of the above-mentioned control technologies have their own inherent bottlenecks both in theory and in application. For example, for adaptive control, there are many stability methods to be chosen from when designing the control rate, but most of them require strong assumptions. For fuzzy control, simple fuzzy processing will lead to a reduction in control precision and poor system dynamic quality. For neural network control, the closed-loop system is stable only at a certain point, and the exponential function of neurons will also lead to a large number of computations and poor real-time performance. Sliding mode variable structure control can easily fall into a chattering state, and it also needs to solve for factors such as velocity, inertia, acceleration, and the switching surface when near the sliding mode surface. For predictive control based on a mathematical model, it is difficult to establish an accurate mathematical model, or even to build a model. Therefore, collective solutions were adopted in this study. The multi-dimensional Taylor network is an inclusive platform and applies to nonlinear control particularly effectively. For example, an adaptive predictive control approach based on the MTN was proposed for the real-time tracking control of single-input single-output nonlinear systems with input time-delay [21]. A control scheme based on the MTN was proposed to achieve the real-time output feedback tracking control of multi-input multi-output non-affine nonlinear time-varying discrete systems relative to the given reference signals with online training [22]. An adaptive control approach based on the MTN was proposed to control nonlinear uncertain time-varying systems with noise disturbances [23]. A predictive control scheme on the basis of the MTN, named MTN predictive compensation control, was proposed for single-input single-output nonlinear systems [24].