Particle swarm optimization solution for roll-off control in radiofrequency ablation of liver tumors: Optimal search for PID controller tuning.

Radiofrequency ablation (RFA) of liver tumors needs precise thermal control to destroy the target tissue while sparing surrounding healthy structures. A key difficulty is the “roll‑off” phenomenon, where a rapid rise in tissue impedance stops further energy delivery. This project tackles that problem by designing a PID controller whose gains are tuned with Particle Swarm Optimization (PSO). First, we model the bio‑heat transfer and electrical‑impedance dynamics of liver tissue during ablation. The PSO algorithm then searches the gain space to find the best proportional, integral, and derivative values. The optimization aims to keep overshoot low, shorten settling time, and avoid premature roll‑off by adjusting power adaptively. We provide guidance for

building the mathematical model of the tissue‑electrode interface and for simulating the closed‑loop control system under different perfusion (blood‑flow) rates. The method evaluates transient response characteristics and thermal‑distribution profiles in detail. It offers a research path for applying intelligent optimization techniques to biomedical control, giving a clear way to test controller robustness and stability in simulated clinical procedures.

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