# Energy-Feedback Load Simulation Algorithm Based on Fuzzy Control
## 4 Load Simulation Fuzzy Controller Design  
In view of the traditional PI setting method of PI parameters to a fixed value, which is unable to adapt to the complex working conditions of the load simulation, as well as the load simulation boost circuit with traditional PI control, which cannot take into account both response speed and the current overshoot problem, this paper proposes a feed-type DC load based on the fuzzy control simulation control algorithm. As a kind of nonlinear controller, a fuzzy controller has little dependence on the accuracy of the mathematical model, and the control experience obtained from practical engineering can be easily expressed by fuzzy rules. At the same time, boost circuit load simulation is a nonlinear system with time-varying parameters [22]. By combining traditional PI setting and fuzzy control, appropriate fuzzy control rules are formulated to improve the dynamic response performance of the system, reduce current overshoot, and reduce the steady-state error of the system. To identify the current response status of the fuzzy controller, the input current error \(e\) and the rate of change of \(ec\) as the input of the fuzzy controller and the fuzzy controller output increment of PI parameters \(\Delta K_{\text{P}}\), \(\Delta K_{\text{I}}\) were selected. The design of the input-current-tracking control algorithm based on fuzzy control is shown in Figure 6.  
Through AD sampling and calculation, the control module obtains the exact amount of error \(e\) between input current \(I_{\text{IN}}\) and current reference value \(I_{\text{IN}\_\_\_\_\_\_\_\_  
rate \(ec\), which is then transformed into the fuzzy quantity in the fuzzy theory domain by multiplying by their respective quantitative factors and mapping to the corresponding fuzzy theory domain. In order to reduce the computational burden of DSP software, the discrete fuzzy theory domain is used for the fuzzy input of the fuzzy controller. At the same time, in order to make the control output more stable, the fuzzy theory domain of output is selected as the continuous fuzzy theory domain. Among them, the fuzzy domain of error and error rate of change is the discrete theoretical domain of level 21; that is, its fuzzy domain is \(\{-10,\,-9,\,\cdot,\,0,\,\cdot,\,9,\,10\}\). The fuzzy comprehensive domain \(\Delta P\) ratio coefficient of variation and integral coefficient variation \(\Delta I\) continuous are both [1; 1]. In order to better describe the relationship between input and output with \"fuzzy\" language, the fuzzy corresponding to input and output needs to be further classified into fuzzy subsets by membership function; the relationship between the input and output fuzzy subsets is described by fuzzy control rules. That is, the fuzzy subset is divided into seven levels, represented by NL (Negative Large), NM (Negative Middle), NS (Negative Small), Z (Zero), PS (Positive Small), PM (Positive Middle), and PL (Positive Large). The membership functions of input \(E\) and \(EC\) of the fuzzy controller are triangular functions, as shown in Figure 7a, which have higher resolution and are conducive to improving the sensitivity of the control. In addition, output \(\Delta P\), \(\Delta I\) type membership function is chosen as a normal distribution function, and as shown in Figure 7b, the change smooth feature makes the output jitter amplitude decrease, and the output control is smoother, with a higher stability of the system. According to the engineering experience, the control rules of the fuzzy controller are formulated. A single fuzzy control rule \(\rm{R_{i}}\) can be expressed as \"If (condition 1 AND condition 2), Then (conclusion 1 AND conclusion 2)\". A complete fuzzy controller rule is formed between each rule by the \"OR\" operator. Figure 7: The membership function of input and output. (**a**) Membership functions of input quantities E and EC. (**b**) E, EC membership function of input and output \(\Delta\) P, \(\Delta\) membership function of the I.