RESUMO
Cancer treatment often involves heat therapy, commonly administered alongside chemotherapy and radiation therapy. The authors address the challenges posed by heat treatment methods and introduce effective control techniques. These approaches enable the precise adjustment of laser radiation over time, ensuring the tumour's core temperature attains an acceptable level with a well-defined transient response. In these control strategies, the input is the actual tumour temperature compared to the desired value, while the output governs laser radiation power. Efficient control methods are explored for regulating tumour temperature in the presence of nanoparticles and laser radiation, validated through simulations on a relevant physiological model. Initially, a Proportional-Integral-Derivative (PID) controller serves as the foundational compensator. The PID controller parameters are optimised using a combination of trial and error and the Imperialist Competitive Algorithm (ICA). ICA, known for its swift convergence and reduced computational complexity, proves instrumental in parameter determination. Furthermore, an intelligent controller based on an artificial neural network is integrated with the PID controller and compared against alternative methods. Simulation results underscore the efficacy of the combined neural network-PID controller in achieving precise temperature control. This comprehensive study illuminates promising avenues for enhancing heat therapy's effectiveness in cancer treatment.
RESUMO
This paper proposes a reasonable, practical, and novel method for designing a robust controller for two degrees of freedom nano-electromechanical scanners based on the combination of quantitative feedback theory (QFT) and the Taguchi method. Although the main primary of this paper devotes to rotational control, an investigation of two-degree freedom (rotational/bending) is studied by employing the Taguchi method. A moveable main plate suspended by two nano-beams over a fixed substrate electrode is used to represent the scanner. The nanoscanner is thoroughly analyzed using the most comprehensive modeling design, considering the elastic, electrical, Casimir force and moment, and squeezes film damping. The nanoscanner's governing equations are initially derived by considering both rotation and deformation. In addition, because the size dependency of materials is significant in ultra-small structures, we developed the constitutive equations within the context of the modified couple stress theory to integrate the effect of scale dependency. Next, system uncertainties have been wholly addressed to achieve an accurate model. As a result, using robust control methods such as quantitative feedback theory to precisely control nano-scanners in the presence of uncertainties is inevitable. The quantitative feedback approach transforms the nonlinear plant into a family of linear uncertain plants in the first part. This is accomplished using a fixed-point theorem, after which appropriate disturbance rejection boundaries are discovered. In this problem, quantitative feedback controllers and checking the system's stability at any frequency and time are intended to solve the tracking problem and the disturbance rejection issue. Due to the uncertainty associated with the system model's complexity and accuracy, we employ a QFT controller to control the system. Moreover, because this system has only one input and two outputs, and changing the controller's gain is complicated, the Taguchi method has been employed to enhance better performance. Nonlinear simulations of the tracking issue are carried out, and the results demonstrate the effectiveness of the developed controllers and prefilters. The findings show that using the suggested approach effectively overcomes the challenges to robust control of a nonlinear rotational nanoscanner, and also the system achieves the best angle and deflection adjustment accuracy.
