## 2 Horizontal Control Strategy
### Drive Control  
The unmanned fully line-controlled distributed drive electric vehicle uses hub motors and only a conventional fuel vehicle model is available in the CarSim 2019 software. In order to build a drive system that is consistent with the properties of the real car motor, the motor is thus modelled externally. The motor model is built using PI control theory. The inputs to the motor model are the target drive torque and wheel velocity, and the actual drive torque is output through the regulation of the PI controller.  
Fuzzy PI Controller Design  
According to the controller input through the fuzzy controller online live-time regulation of the proportional coefficient (P), integral coefficient (I), the principle is illustrated in Figure 3.  
Figure 3: The principle of the fuzzy PI controller.  
Figure 2: Vertical control strategy.  
As in Figure 3, the controller's inputs are \(e\) (error of system) and \(ec\) (the rate of error change in the system). Fuzzy rules are formulated for reasoning decisions, and the two parameters of the PI controller are adjusted in live time with parameter changes of \(\Delta K_{p}\) and \(\Delta K_{i}\). The output is obtained by adding the changes to the initial values of the parameter values.  
\[\left\{\begin{array}{l}K_{p}=K_{p0}+\Delta K_{p}\\ K_{i}=K_{i0}+\Delta K_{i}\end{array}\right. \tag{1}\]  
In Equation (1), \(K_{p0}\) and \(K_{i0}\) are the initial values. \(\Delta K_{p}\) and \(\Delta K_{i}\) are the variation amounts rectified based on fuzzy control rules.  
The \(e\) and \(ec\) are:  
\[e=v_{ref}-v \tag{2}\]  
\[ec=d/dt(e_{v}) \tag{3}\]  
The range of speed deviation was formulated as [\(-\)9, 9] and its theoretical domain range was set to [\(-\)9, 9]; the range of speed deviation rate was [\(-\)3, 3] and its theoretical domain range was set to [\(-\)3, 3]; The theoretical domains of both \(\Delta K_{p}\) and \(\Delta K_{i}\) are [\(-\)10, 10]; the input and output quantities are used as triangular affiliation functions, as depicted in Figure 4.  
As illustrated in Figure 4, the fuzzy set is divided into [negative large, negative average, negative small, zero, positive small, positive average, positive large], i.e., {NL, NA, NS, ZO, PS, PA, PL}.  
The vertical drive controller is mainly in accordance with the vague '\(e\)' and '\(ec\)' inference of the change values \(K_{p}\) and \(K_{i}\) the correction of the real-time PI data. When the vehicle is starting and stopping, the speed deviation is large, at which point the system  
Figure 4: Input and output belong to the membership degree graph. (**a**) E membership degree; (**b**) EC membership degree; (**c**) \(k_{i}\) membership degree; (**d**) \(k_{p}\) membership degree.  
selects a somewhat larger value for \(K_{p}\) in order to obtain a larger driving force to quickly eliminate errors; as E and the EC become medium, \(K_{p}\) is reduced somewhat and a smaller \(K_{i}\) is chosen; when the actual velocity is infinitely near the desired speed and the e is small, the output value is zero to prevent excessive speed control. The resulting fuzzy rules are indicated in Tables 1 and 2.  
The fuzzy rule plane diagram of \(K_{p}\), \(K_{i}\) is depicted in Figure 5: