# Resonant Power Supply for Plasma Cleaning Based on Fuzzy Logic Power Tracking
## 3 Resonant Power Supply Control Method for Plasma Cleaning
The control quantity is consistent with the power change quantity, that is, when the system is close to the maximum power point, the power change quantity is small, and the disturbance quantity should be kept small, otherwise the control quantity should be kept large. The fuzzy control rules are shown in Table 2 and Figure 9, and the corresponding 3D surface of the fuzzy control rules is shown in Figure 8 [17]. Figure 2:2 Membership degree curve: (**a**) Membership curve of \(E\); (**b**) Membership curve of \(EC\); (**c**) Membership curve of \(C\). After the above steps, the output quantity C needs to be de-fuzzified and converted into the actual control quantity, so as to realize the modulation of the driving frequency. In this paper, the gravity center method is adopted to solve the ambiguity, and its formula is:  
Figure 2:3  
where, \(c\) is the output of the fuzzy controller, \(U_{i}\) is the center value of the output corresponding to the membership function interval, and \(\mu(U_{i})\) is the membership degree corresponding to \(U_{i}\)[18]. A model is built in Simulink to simulate the above hybrid modulation algorithm and the maximum power tracking algorithm based on fuzzy PFM, as shown in Figure 2:4 The upper part of the figure consists of the main circuit of the phase-shifted full bridge and its load. In order to simulate the mutation of the load caused by perturbation, capacitors C\({}_{1}\) and C\({}_{2}\) are connected in parallel or in series at the equivalent load. They are controlled by pulse signals G\({}_{1}\) and G\({}_{2}\) and switch transistors Q\({}_{5}\) and Q\({}_{6}\), respectively, so as to introduce perturbation at specific time points. The lower part is a hybrid modulation algorithm, including a frequency estimator, a PSPWM controller, a fuzzy PFM controller, and a drive signal generator. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**EC**} & \multicolumn{6}{c}{**E**} \\ \cline{2-9}  & **NB** & **NM** & **NS** & **ZE** & **PS** & **PM** & **PB** \\ \hline NB & PB & PM & PS & ZE & NS & NM & NB \\ NM & PB & PM & PS & ZE & NS & NM & NB \\ NS & PB & PM & PS & ZE & NS & NM & NB \\ PS & NB & NM & NS & ZE & PS & PM & PB \\ PM & NB & NM & NS & ZE & PS & PM & PB \\ PB & NB & NM & NS & ZE & PS & PM & PB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fuzzy control rules. Figure 9: 3D output surface of fuzzy control rules. Figure 11 shows the internal structure of a frequency estimator. This circuit is used to obtain the working frequency of PWM. Firstly, the single PWM (Pulse Width Modulation) waveform is integrated, and then the peak point is sampled by the delay circuit and comparison function. Finally, the working frequency of the corresponding time point is derived by the duty cycle. Figure 12 shows the internal structure of a PSPWM controller. The module detects the frequency and power error of the system in real time. Once the power error is above 100 W, or the frequency deviates to the upper and lower limits during frequency modulation, namely 2.5 Hz and 6.5 kHz, the module starts to work and realizes power control by adjusting the phase shift angle [19]. Figure 11: Internal structure of the frequency estimator. Figure 10: Simulation model of hybrid modulation algorithm. The internal structure of the fuzzy PFM controller is shown in Figure 13. Firstly, the power and frequency of the system are sampled and preprocessed, and the variation value is obtained and input into the fuzzy controller. The output value, as the variation of frequency, is added to the initial frequency and sent to the PFM modulator. When the power error is less than 100 W, or the phase shift angle deviates to 20\({}^{\circ}\) or 70\({}^{\circ}\), the module starts to work to realize the maximum power tracking under the current duty cycle of the system, prevents the system from deviating from the resonant frequency due to external perturbation, and ensures the working efficiency of the system. Figure 14 shows the internal structure of the driver signal generator.