Expert knowledge-based peak current mode control of electrosurgical generators for improved output power regulation
Rafiq, Muhammad Mohsin (University of IT & Emerging Sciences)
Ibeas, Asier (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Ullah, Nasim (Taif University. Department of Electrical Engineering)

Data:	2023
Resum:	Electrosurgical generators (ESG) are widely used in medical procedures to cut and coagulate tissue. Accurate control of the output power is crucial for surgical success, but can be challenging to achieve. In this paper, a novel expert knowledge-based peak current mode controller (EK-PCMC) is proposed to regulate the output power of an ESG. The EK-PCMC leverages expert knowledge to adapt to changes in tissue impedance during surgical procedures. We compared the performance of the EK-PCMC with the classical peak current mode controller (PCMC) and fuzzy PID controller. The results demonstrate that the EK-PCMC significantly outperformed the PCMC, reducing the integral square error (ISE) and integral absolute error (IAE) by a factor of 3. 88 and 4. 86, respectively. In addition, the EK-PCMC outperformed the fuzzy PID controller in terms of transient response and steady-state performance. Our study highlights the effectiveness of the proposed EK-PCMC in improving the regulation of the output power of an ESG and improving surgical outcomes.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Electro-surgery ; Tissue burning ; Tissue impedance ; Peak current mode control ; Expert knowledge control
Publicat a:	Journal of Electrical Bioimpedance, Vol. 14, Issue 1 (January 2023) , p. 32-46, ISSN 1891-5469

DOI: 10.2478/joeb-2023-0005
PMID: 38025910

15 p, 1.5 MB

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