# Energy-Feedback Load Simulation Algorithm Based on Fuzzy Control
## 7 Conclusions  
The main achievements and conclusions of this paper are as follows:  
1. The control mechanism of the high-efficiency regenerative DC electronic load under different load simulation modes was studied. The control objects of the three load simulation modes of constant current, constant resistance, and constant power were normalized and converted into the tracking control of the input current, which simplifies the design of the subsequent load simulation control algorithm.
2. Based on the state average method, a small signal model of load simulation was established to obtain the transfer function of input current to drive duty ratio, which lays a research foundation for control system calibration. According to the design requirements of the control system, the PI controller was designed based on the phase margin PI parameter setting method. After the series PI correction, the system had good dynamic response performance and steady-state accuracy.
3. In view of the problem that the traditional PI controller cannot take into account the response speed and input current overshoot and cannot adapt to complex working conditions, a load simulation control strategy combining fuzzy control and traditional PI control was proposed. The method of identifying the current response state by error E and error change rate EC and the control rule design of fuzzy controller corresponding to the response state were studied. Finally, the load simulation control algorithm based on fuzzy control had a shorter dynamic response time and smaller input current overshoot, which is beneficial to reducing the number of protection mistrigger times while responding quickly.  
There are still some deficiencies in this paper that need to be improved. The improvement ideas for the next step are as follows:  
1. Optimize the signal sampling and filtering scheme, optimize the RC filtering parameters of the sampling circuit, strengthen the filtering effect of the software algorithm, and reduce the influence of accidental errors on the sampling accuracy.
2. Further optimize the fuzzy control rules of load simulation and add power feedforward into the load simulation control strategy so as to improve the robustness of the system and reduce the DC bus double-frequency voltage ripple.  
Figure 14: Load analog resistance relative error curve.  
**Author Contributions:** X.C.: Methodology, Formal analysis, Writing--original draft. Z.F.: Supervision. H.X.: Writing--review and editing, Supervision. J.X.: Project administration, Funding acquisition. H.L., N.O.: Investigation. G.Z.: Data curation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project is supported by the Fujian Educational and Scientific Research Project of Young and Middle-aged Teachers (No. JAT190749), DongGuan Innovative Research Team Program (No. 2017360004004), National Natural Science Foundation of China (No. 51875213), LongYan Qimai Science and Technology Innovation Fund (No. 2019 LYF10003), Shanghang Qimai Science and Technology Innovation Fund (No. 2020 SHQM10), and Dongguan Science and technology equipment mobilization project (No. KZ2019-6, KZ2020-01).  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** All data, models, and code generated or used during the study appear in the submitted article.  
**Conflicts of Interest:** This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood the journal's policies, and we believe that neither the manuscript nor the study violates any of them.