## 3 Experiments and Discussions
### Discussions  
The ANFIS-PID controller mainly optimises the problems of overshoot and difficult parameter adjustment of PID controllers in RFA temperature control. From the FIL simulation and ex vivo experimental results, it can be concluded that the ANFIS-PID controller has an advantage over the PID controller in terms of stability performance and tracking ability in the case of normal use, target temperature variation, and imposed disturbances. However, the ANFIS-PID controller and the PID controller may have some steady-state errors in the final state during the test, which may be partly due to the fact that both controllers are \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Overshoot** & **Undershoot** & **Rise Time(s)** & **Steady-State** \\  & **(\%)** & **(\%)** & **(0–0.95)** & **Error (\({}^{\circ}\)C)** \\ \hline PID & 5.25 & 4 & 13.12 & +1.9, \(-\)1.6 \\ ANFIS-PID & 0 & 1.25 & 74.08 & +0.4, \(-\)0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ex vivo experiments with two controller parameters. Figure 7: The temperature rise curves of the two controllers in ex vivo experiments. implemented using fixed-point decimals based on FPGAs, resulting in a loss of accuracy in the intermediate process calculations. In the simulation, the constitutive principle of the transfer function used is the same as that of [PERSON] et al. [29], which consists of two parts: 1. the relationship between the voltage of the RFA device acting on the tissue and the temperature of the tissue and 2. the relationship between the initial low-amplitude RF signal from the RFA device and the time of the RF signal applied to the tissue. However, due to the different RFA devices used, the transfer function of the first part of [PERSON] et al. [29]'s approach consists of two parts, while that of the present paper consists of one part, and thus there is a difference between the overall transfer functions of the two. Therefore, the parameters of the PID control algorithm of [PERSON] et al. [29] cannot be directly adopted, and its parameters may not work as well as they should in real ablations where there is a delay. In addition, due to the complexity of the tissues that RFA works on, there is a hysteresis between the voltage of the applied RF signal and the tissue temperature, which cannot be accurately obtained. At the same time, during the temperature rise in the actual ablation, changes in the tissue parameters cause a change in the output power of the ablation device, resulting in a slower temperature rise time, while the simulation is in the ideal state where the tissue parameters are unchanged. As a result, there is some error between the simulation results and the experimental results. Nevertheless, the trends are similar: the PID controller has a faster response and the ANFIS-PID has better stability. In the ex vivo experimental tests, the PID controller has larger fluctuations compared to the ANFIS-PID controller, which may be partly due to the fact that the FPGA cannot directly perform decimal arithmetic when implementing the two controllers, which will cause some errors when using fixed-point decimal arithmetic; this is especially more obvious when the error between the actual temperature and the target temperature is small. Therefore, when implementing ANFIS-PID, the learning rate of the consequence parameter is set in segments, with the condition that the actual temperature differs from the target temperature by 3 \({}^{\circ}\)C. A larger learning rate is selected when the input error is small, and, conversely, a smaller learning rate is used to improve control accuracy. The difference in rise time between ANFIS-PID and PID controllers may be due to the fact that the learning rate of ANFIS-PID may not be set appropriately when the error between the actual temperature and the set temperature is greater than 3 \({}^{\circ}\)C. Therefore, the process of selecting the learning rate is complex.