Simulation PID gains and performance indexes.

The study investigates the efficacy of a bioinspired Particle Swarm Optimization (PSO) approach for PID controller tuning in Radiofrequency Ablation (RFA) for liver tumors. Ex-vivo experiments were conducted, yielding a 9th order continuous-time transfer function. PSO was applied to optimize PID parameters, achieving outstanding simulation results: 0.605% overshoot, 0.314 seconds rise time, and 2.87 seconds settling time for a unit step input. Statistical analysis of 19 simulations revealed PID gains: Kp (mean: 5.86, variance: 4.22, standard deviation: 2.05), Ki (mean: 9.89, variance: 0.048, standard deviation: 0.22), Kd (mean: 0.57, variance: 0.021, standard deviation: 0.14) and ANOVA analysis for the 19 experiments yielded a p-value ≪ 0.05. The bioinspired PSO-based PID controller demonstrated remarkable potential in mitigating roll-off effects during RFA, reducing the risk of incomplete tumor ablation. These findings have significant implications for improving clinical outcomes in hepatocellular carcinoma management, including reduced recurrence rates and minimized collateral damage. The PSO-based PID tuning strategy offers a practical solution to enhance RFA effectiveness, contributing to the advancement of radiofrequency ablation techniques.

本研究探究了仿生粒子群优化（Particle Swarm Optimization，PSO）方法在肝肿瘤射频消融（Radiofrequency Ablation，RFA）场景下用于PID控制器参数整定的有效性。本研究通过离体实验获取了九阶连续时间传递函数，随后采用PSO算法优化PID参数，获得了优异的仿真性能：针对单位阶跃输入，系统超调量为0.605%，上升时间0.314秒，调节时间2.87秒。对19组仿真实验的统计分析结果表明，PID参数增益分别为：比例增益Kp（均值：5.86，方差：4.22，标准差：2.05）、积分增益Ki（均值：9.89，方差：0.048，标准差：0.22）、微分增益Kd（均值：0.57，方差：0.021，标准差：0.14）；针对19组实验的方差分析（Analysis of Variance，ANOVA）结果显示p值远小于0.05。基于仿生PSO的PID控制器在缓解RFA过程中的滚降效应、降低肿瘤消融不完全风险方面展现出显著应用潜力。上述研究结果对改善肝细胞癌诊疗的临床结局具有重要价值，可有效降低复发率并减轻手术附带损伤。基于PSO的PID参数整定策略为提升RFA临床疗效提供了切实可行的解决方案，有助于推动射频消融技术的发展。