The　radiofrequency　ablation　temperature　system　is　characterised　by　its　time-varying,　non-linear,　and　hysteretic　nature.　The　application　of　PID　controllers　to　the　control　of　radiofrequency　ablation　temperature　systems　has　a　number　of　challenges,　including　overshoot,　dependence　on　high-precision　mathematical　models,　and　difficulty　in　parameter　tuning.　Therefore,　in　order　to　improve　the　effectiveness　of　radiofrequency　ablation　temperature　control,　an　adaptive　network-based　fuzzy　inference　system　combined　with　an　incremental　PID　controller　was　used　to　optimise　the　shortcomings　of　the　PID　controller　in　radiofrequency　ablation　temperature　control.　At　the　same　time,　the　learning　rate　at　the　time　of　updating　the　consequence　parameters　was　set　by　segmentation　to　solve　the　problem　of　poor　control　accuracy　when　the　ANFIS-PID　controller　is　implemented　based　on　FPGA　fixed-point　decimals.　Based　on　FPGA-in-the-loop　simulation　experiments　and　ex　vivo　experiments,　the　effectiveness　of　the　ANFIS-PID　controller　in　the　temperature　control　of　radiofrequency　ablation　was　verified　and　compared　with　the　PID　controller　under　the　same　conditions.　The　experimental　results　show　that　the　ANFIS-PID　controller　has　a　superior　performance　in　terms　of　tracking　capability　and　stability　compared　with　the　PID　controller.