# Energy-Feedback Load Simulation Algorithm Based on Fuzzy Control
## 5 Load Simulation Fuzzy Control Algorithm Simulation Analysis  
In order to verify the dynamic response and steady-state performance of the load simulation algorithm based on fuzzy control, as well as the rationality of the designed fuzzy controller and the formulated fuzzy control rules, a Simulink simulation model of load simulation boost circuit control was established, as shown in Figure 8.  
Boost circuit input voltage \(V_{\text{IN}}\) is 200 V, output voltage \(V_{\text{OUT}}\) = 400 V, boost inductance \(L\) = 1.04 mh, and switching frequency \(f\) = 20 kHz. The effective value \(I_{\text{IN\_RMS}}\) of the input current tracked by the load simulation control algorithm is obtained from the RMS module of Simulink. The reference value of the input current is 15 A, and the discrete simulation step size is 0.1 microns. The fuzzy control rules in Tables 1 and 2 are selected for the fuzzy controller in the simulation model. In the simulation model, the traditional PI controller adopts the incremental digital PI controller to calculate the duty cycle of the boost switch tube drive waveform and then generates the corresponding PWM drive signal through its own PWM generator boost.  
Figure 8: Fuzzy-control-based load simulation control strategy Simulink simulation model.  
The reference value of the input current of the load simulation algorithm based on fuzzy control is 15 A, and the simulation results of Simulink are shown in Figure 9. In the 15 A step response process, the maximum value of the input current is 16.12 A, the value of the overshot is \(\sigma\%\) = 7.47%, and the adjustment time \(t_{S}\) = 0.98 ms was controlled at 15 A plus or minus 5%. Compared with the load simulation control algorithm using only traditional PI control, the load simulation algorithm based on fuzzy control has a shorter dynamic response time and smaller overdose of input current. At 2.5 ms, the switching between fuzzy control rules is not smooth enough, which leads to slight jitter. The minimum current value at jitter is 14.76 A, and the relative error with the reference value of input current is only 1.6%, which quickly returns to the steady state. When the load simulation algorithm controlled by traditional PI is used to work stably, the input current is controlled within the range of 15 \(\pm\) 0.05 A. When fuzzy control is added, the boost circuit stabilizes, and the input current is controlled within the range of 15 \(\pm\) 0.02 A; thus it can be seen that fuzzy control enhances the integral link in the steady state to reduce steady-state error.  
In order to further study the dynamic response performance of the system when the load simulation conditions change, at 0.006 s, the reference value of the input current changes from 15 A to 8 A. At 0.012 s, the reference value of input current changes from 8 A to 12 A, and the simulation results are shown in Figures 10-13. The adjustment time for switching from 15 A to 8 A is \(t_{S}\) = 0.26 ms, and the minimum input current is 7.78 A. The adjustment time from 8 A to 12 A is \(t_{S}\) = 0.23 ms, the maximum input current is 12.05 A, and the input current overshoot is only 0.05 A when switching. The results show that the load simulation control algorithm based on fuzzy control not only improves the dynamic response performance of the system but also reduces the steady-state error of the system, which is conducive to improving the accuracy of load simulation.  
Figure 10: Fuzzy-control-based load simulation strategy variable load Simulink simulation results.  
Figure 9: Improved zero-beat control strategy output current and voltage waveforms.  
Figure 11: Load simulation function test. (**a**) Constant I function test; (**b**) constant R function test; (**c**) constant P function test; (**d**) variable load function test.  
Figure 12: Load simulation current relative error curve.