Particle swarm optimization solution for roll-off control in radiofrequency ablation of liver tumors: Optimal search for PID controller tuning DOI Creative Commons
Rafael Mendes Faria, Suélia de Siqueira Rodrigues Fleury Rosa,

Gustavo Adolfo Marcelino de Almeida Nunes

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(6), P. e0300445 - e0300445

Published: June 26, 2024

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

Language: Английский

Particle swarm optimization solution for roll-off control in radiofrequency ablation of liver tumors: Optimal search for PID controller tuning DOI Creative Commons
Rafael Mendes Faria, Suélia de Siqueira Rodrigues Fleury Rosa,

Gustavo Adolfo Marcelino de Almeida Nunes

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(6), P. e0300445 - e0300445

Published: June 26, 2024

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

Language: Английский

Citations

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