||Research on Intelligent Control and System Modeling to the Electromagnetic Induction Hyperthermia
||Department of Electrical Engineering
finite element method (FEM)
adaptive network fuzzy inference system (ANFIS)
recurrent neural network (RNN)
fuzzy logic control (FLC)
fuzzy model reference learning control (FMRLC)
The aim of this dissertation involved developing a temperature controller and system modeling for predicting the temperature response of tissues in electromagnetic induction hyperthermia (EIH). There are two critical challenges when applied to deep-seated tissue using EIH, i.e., the temperature may not be measured accurately and the temperature control is susceptible to disturbance due to unpredictable plant parameter variations. The finite element method (FEM) was suitable for coupled analysis with electromagnetic fields and heat transfer, which could be used to predict temperature profiles in deep tissue implanted with magnetic materials. To improve the accuracy of the FEM model, an adaptive network fuzzy inference system (ANFIS) model was implemented on the basis of measured data and simulated data, which were generated by the FEM model. The ANFIS model can provide a large number of testing data to optimize the parameters in the FEM model, and it can be used to expedite the optimization process. Moreover, system identification for EIH was analysed and selected with recurrent neural networks models to approximate various conditions of the temperature process. The recurrent neural network was useful for establishing the temperature prediction model with the advantages of simple design and stable efficacy.
A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The EIH system generates an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating has few side effects when it is extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with gain tuning mechanism was implemented to obtain high control performance in a wider range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. The proposed SLFLC was compared to the classical self-tuning fuzzy logic controller (STFLC) and fuzzy model reference learning control (FMRLC). Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicate that the proposed controller shows greater robustness and excellent adaptability with respect to temperature control of the EIH system.
Table of Contents vi
List of Tables viii
List of Figures ix
Chapter 1 Introduction 1
1.1. Background 1
1.2. Motivation 3
1.3. Dissertation Organization 6
Chapter 2 Modeling of Electromagnetic Induction Hyperthermia for temperature Prediction and Optimization 8
2.1. Introduction 8
2.2. Analytical and Numerical Methods 8
2.3. Modeling Based on Finite Element Methods 15
2.4. Adaptive Network Fuzzy Inference System 21
2.5. Optimization Methods Using Genetic Algorithms 24
2.6. Experimental Results and Discussion 26
2.7. Summary 31
Chapter 3 System Identification using Neural Network for Electromagnetic Thermotherapy Systems 32
3.1. Introduction 32
3.2. Modeling Based on Neural Networks 33
3.3. Elman Neural Network 34
3.4. Experimental Results and Discussion 36
3.5. Summary 41
Chapter 4 Temperature Controller for Magnetic Induction Hyperthermia Using PID and Fuzzy Logic Control 42
4.1. Introduction 42
4.2. Inverter with Transformer 42
4.3. Control of COMSOL using MATLAB 47
4.4. PID Control 48
4.5. Fuzzy Logic Control 51
4.6. Simulation and Experimental Results 52
4.7. Summary 60
Chapter 5 Temperature Controller for Electromagnetic Induction Hyperthermia Using Self- Tuning and Learning Fuzzy Logic Control 62
5.1. Introduction 62
5.2. Equivalent Treatment Time Calculation 62
5.3. Self-Tuning Fuzzy Logic Control 64
5.4. Proposed Self-Learning Fuzzy Logic Control 69
5.5. Experiment System 77
5.6. Experimental Results and Discussion 80
5.7. Summary 92
Chapter 6 Conclusions and Future Works 93
6.1. Conclusions 93
6.2. Future Works 94
Publication List 103
 Y. Ishihara, Y. Gotanda, N. Wadamori, and J. Matsuda, “Hyperthermia applicator based on a reentrant cavity for localized head and neck tumors,” Rev. Sci. Instrum., vol. 78, no. 2, 024301, Feb. 2007.
 P. R. Stauffer, “Evolving technology for thermal therapy of cancer,” Int. J. Hyperthermia., vol. 21, no. 8, pp. 731–744, May. 2011.
 A. J. Fenn, Adaptive Phased Array Thermotherapy for Cancer. London, U.K.: Artech House, 2008.
 H. Wang, X. Li, X. Xi, B. Hu, L. Zhao, and Y. Liao, “Effects of magnetic induction hyperthermia and radiotherapy alone or combined on a murine 4T1 metastatic breast cancer model,” Int. J. Hyperthermia, vol. 27, no. 6, pp. 563–572, Aug. 2011.
 P. Moroz, S. K. Jones, and B. N. Gray, “Magnetically mediated hyperthermia: Current status and future directions,” Int. J. Hyperthermia, vol. 18, no. 4, pp. 267–284, Jul. 2002.
 P. R. Stauffer, T. C. Cetas, and R. C. Jones, “Magnetic induction heating of ferromagnetic implants for inducing localized hyperthermia in deep-seated tumors,” IEEE Trans. Biomed. Eng., vol. 31, no. 2, pp. 235–251, Feb. 1984.
 S. C. Huang, J. W. Kang, H. W. Tsai, Y. S. Shan, X. Z. Lin, and G. B. Lee, “Electromagnetic thermotherapy for deep organ ablation by using a needle array under a synchronized-coil system,” IEEE Trans. Biomed. Eng. vol. 61, no. 11, pp. 2733–2739, Nov. 2014.
 Y. Lv, Z. Deng and J. Liu, “3-D numerical study on the induced heating effects of embedded micro/nanoparticles on human body subject to external medical electromagnetic field,” IEEE Trans. Nanobiosci., vol. 4, no. 4, pp. 284–294, Dec. 2005.
 A. L. Glover, J. B. Bennett, J. S. Pritchett, S. M. Nikles, D. E. Nikles, J. A. Nikles, and C. S. Brazel, “Magnetic heating of iron oxide nanoparticles and magnetic micelles for cancer therapy,” IEEE Trans. Magn., vol. 43, no. 1, pp. 231–235, Jan. 2013.
 V. Mateev, I. Marinova, and Y. Saito, “Coupled field modeling of ferrofluid heating in tumor tissue,” IEEE Trans. Magn., vol. 49, no. 5, pp. 1793–1796, May 2013.
 H. Rahn, S. Schenk, H. Engler, and S. Odenbach, “Tissue model for the study of heat transition during magnetic heating treatment,” IEEE Trans. Magn., vol. 49, no. 1, pp. 224–249, Jan. 2013.
 L. Y. Zhao, J. Y. Liu, W. W. Ouyang, D. Y. Li, L. Li, L. Y. Li, and J. T. Tang, “Magnetic-mediated hyperthermia for cancer treatment: Research progress and clinical trials,” Chinese Phys., vol. 22, no. 10, 108104, Oct. 2013.
 C. C. Tai, C. C. Chen, C. C. Kuo, F. W. Lin, C. J. Chang, Y. H. Chen, and W. C. Wang, “Deep-magnetic-field generator using flexible laminated Copper for thermotherapy applications,” IEEE Trans. Magn., vol. 50, no. 11, Nov. 2014.
 R. Zuchini, H. W. Tsai, C. Y. Chen, C. H. Huang, S. C. Huang, G. B. Lee, C. F. Huang, and X. Z. Lin, “Electromagnetic thermotherapy using fine needles for hepatoma treatment,” Eur. J. Surg. Oncol., vol. 37, no. 7, pp. 604–610, July 2011.
 C. Gómez-Polo, S. Larumbe, J. I. Pérez-Landazábal, J. M. Pastor, J. Olivera, and J. Soto-Armañanzas, “Magnetic induction heating of FeCr nanocrystalline alloys,” J. Magn. Magn. Mater., vol. 324, no. 11, pp. 1897–1901, June 2012.
 P. Kitak, A. Glotic, and I. Ticar, “Heat transfer coefficients determination of numerical model by using particle swarm optimization,” IEEE Trans. Magn., vol. 50, no. 2, Feb. 2014.
 P. di Barba, F. Dughiero, M. Forzan, and E. Sieni, “A Paretian approach to optimal design with uncertainties. Application in induction heating”, IEEE Trans. Magn., vol. 50, no. 2, Feb. 2014.
 M. Hettegger, B. Streibl, O. Biro, and H. Neudorfer, “Measurements and simulations of the convective heat transfer coefficients on the end windings of an electrical machine,” IEEE Trans. Ind. Electron., vol. 59, no. 5, pp. 2299–2308, May 2012
 J. A. Paulus, J. S. Richardson, R. D. Tucker, and J. B. Park, “Evaluation of inductively heated ferromagnetic alloy implants for therapeutic interstitial hyperthermia,” IEEE Trans. Biomed. Eng., vol. 43, no. 4, pp. 406–413, Apr. 1996.
 F. Dughiero and S. Corazza, “Numerical simulation of thermal disposition with induction heating used for oncological hyperthermic treatment,” Med. Biol. Eng. Comput., vol.43, no. 1, pp. 40–46, Jan. 2005.
 M. R. Barati, C. Selomulya, K. G. Sandeman, and K. Suzuki, “Extraordinary induction heating effect near the first order Curie transition,” Appl. Phys. Lett., vol. 105, 162412, Oct. 2014.
 K. Ogata, Modern Control Engineering. Englewood Cliffs, NJ: Prentice-Hall., 2010.
 W. L. Lin, R. B. Roemer, and K. Hynynen, “Theoretical and experimental evaluation of a temperature controller for scanned focused ultrasound hyperthermia,” Med. Phys., vol. 17, no. 4, pp. 615–625, July 1990.
 J. K. Potocki and H. S. Tharp, “Reduced-order modeling for hyperthermia control,” IEEE Trans. Biomed. Eng., vol. 39, no. 12, pp. 1265–1273, Dec. 1992.
 M. Mattingly, R. B. Roemer, and S. Devasia, “Exact temperature tracking for hyperthermia: A model-based approach,” IEEE Trans. Control Syst. Technol., vol. 8, no. 6, pp. 979–992, Nov. 2000.
 O. Lucía, P. Maussion, E. J. Dede, and J. M. Burdío, “Induction heating technology and its applications: Past developments, current technology, and future challenges,” IEEE Trans. Ind. Electron., vol. 61, no. 5, pp. 2509–2520, May 2014.
 W. C. Wang, G. E. Lin, C. C. Tai, Y. J. Lan, and T. C. Yu, “Real-time prediction of temperature for electromagnetic heating therapy in deep-seated tissue,” IEEE Trans. Magn., vol. 52, no. 3, Mar. 2016.
 J. P. McGahan and G. D. Dodd, “Radiofrequency ablation of the liver: current status,” Am J Roentgenol., vol. 176, no. 1, pp. 3–16, Jan. 2001.
 E. J. Davies, Conduction and Induction Heating. London, U.K.: Peregrinus, 1990.
 S. Lupi, M. Forzan, and A. Alifierov, Induction and Direct Resistance Heating‒Theory and Numerical Modeling. Switzerland: Springer, 2015.
 C. V. Dodd and W. E. Deeds, “Analytical solutions to eddy current probe coil problems,” J. Appl. Phys., vol. 39, no. 6, pp. 2829–2938, May 1968.
 J. Donea, S. Giuliani, and A. Philippe, “Finite elements in the solution of electromagnetic induction problems,” Int. J. Num. Meth. Eng., vol. 8, no. 2, pp. 359–367, 1974.
 H. H. Pennes, “Analysis of tissue and arterial blood temperature in the resting human forearm,” J. Appl. Physiol., vol. 1, no. 2, pp. 93–122, Aug. 1948.
 J. Werner and M. Buse, “Temperature profiles with respect to inhomogeneity and geometry of the human body,” J. Appl. Physiol., vol. 65, no. 3, pp. 1110–1118, Sep. 1988.
 D. Arora, M. Skliar, and R. B. Roemer, “Model-predictive control of hyperthermia treatments,” IEEE Trans. Biomed. Eng., vol. 49, no. 7, pp. 629–639, July 2002.
 R. B. Roemer, A. M. Fletcher, and T. C. Cetas, “Obtaining local SAR and blood perfusion data from temperature measurements: steady state and transient techniques compared,” Int. J. Radiat. Oncol. Biol. Phys., vol. 11, no. 8, pp.1539–1550, Aug. 1985.
 A. Y. Goharrizi, W. A. N’djin, R. Kwong, and R. Chopra “Development of a new control strategy for 3D MRI-controlled interstitial ultrasound cancer therapy,” Med. Phys., vol. 40, no. 3, 033301, Mar. 2013.
 V. S. Nemkov and R. Goldstein, “Computer simulation for fundamental study and practical solutions to induction heating problems,” The international journal for computation and mathematics in electrical and electronic engineering, vol. 22, no. 1, pp. 181–191, Mar. 2003.
 J. S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665–685, May/June 1993.
 J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: The Univ. Michigan Press, 1975.
 R. L. Haupt and D. H. Werner, Genetic Algorithms in Electromagnetics. Hoboken, NJ: Wiley–IEEE Press, 2007.
 S. Yildirim, “New neural networks for adaptive control of robot manipulators,” in Proc. IEEE Int. Conf. Neural Netw., Houston, TX, 1997, pp. 1727–1731.
 J. Wang, J. Wang, M. Zeng, and J. Wang, “Prediction of internet traffic based on Elman neural network,” in Proc. IEEE Int. Conf. Contorl and Deci., Guilin, 2009, pp. 1248–1252.
 Y. C. Cheng, W. M. Qi, and W. Y. Cai, “Dynamic properties of Elman and modified Elman neural network,”, in Proc. IEEE Int. Conf. Machine Learning and Cybe., Beijing, 2002, pp. 637–640.
 R. Köker, “Design and performance of an intelligent predictive controller for a six-degree-of-freedom robot using the Elman network,” Information Sci., vol. 176, no. 12, pp. 1781–1799, June 2005.
 J. B. Mbede, X. Huang, and M. Wang, “Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators,” IEEE Trans. Fuzzy Syst., vol. 11, no.2, pp. 249–261, Apr. 2003.
 H. Liu, S. Wang, and P. Ouyang, “Fault diagnosis based on improved Elman neural network for a hydraulic servo system,” in Proc. Int. Conf. Robot. Autom. Mechatron., Bangkok, 2006, pp. 1–6.
 F. J. Lin, S. Y. Chen, and K. K. Shyu, “Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system,” IEEE Trans. Neural Networks, vol. 20, no. 6, pp. 938–951, June 2009.
 C. F. Juang and J. S. Chen, “A recurrent fuzzy-network-based inverse modeling method for a temperature system control,” IEEE Trans. Syst., Man, Cyberen. C, Appl. Rev., vol. 37, no. 3, pp. 410–417, May 2007.
 C. F. Juang and J. S. Chen, “Water bath temperature control by a recurrent fuzzy controller and its FPGA implementation,” IEEE Trans. Ind. Electron., vol. 53, no. 3, pp. 941–949, June 2006.
 J. L. Elman, “Finding structure in time,” Cognitive Sci., vol. 14, no. 2, pp. 179–211, Mar. 1990.
 S. Haykin, Neural Networks and learning machines. Upper Saddle River, NJ: Pearson Prentice Hall, 2009.
 P. S. Sastry, G. Santharam, and K. P. Unnikrishnan, “Memory neural networks for identification and control of dynamic systems,” IEEE Trans. Neural Networks, vol. 5, no. 2, pp. 306–319, Mar. 1994.
 P. Li, F. Yan, C. Ge, and M. Zhang, “Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model,” Rev. Sci. Instrum., vol. 83, no. 8, 085114, Aug. 2012.
 S. Sanaye, M. Dehghandokht, and A. Fartaj, “Temperature control of a cabin in an automobile using thermal modeling and fuzzy controller,” Appl. Energy, vol. 97, pp. 860–868, Sep. 2012.
 P. Arpaia, L. Bottura, G. Montenero, and C. Svelto, “Smart monitoring system based on adaptive current control for superconducting cable test,” Rev. Sci. Instrum., vol. 85, no. 12, 125111, 2014.
 D. Driankov, H. Hellendoorn, and M. Reinfrank, An Introduction to Fuzzy Control. Germany, Berlin: Springer-Verlag, 1993.
 R. K. Mudi and N. R. Pal, “A robust self-tuning scheme for PI- and PD-type fuzzy controllers,” IEEE Trans. Fuzzy Syst., vol. 7, no. 1, pp. 2–16, Feb. 1999.
 S. C. Wang and Y. H. Liu, “A modified PI-like fuzzy logic controller for switched reluctance motor drives,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1812–1825, May 2011.
 J. R. Layne and K. M. Passino, “Fuzzy model reference learning control,” J. Intell. Fuzz. Syst., vol. 4, no. 1, pp. 33–47, Jan. 1996.
 S.A. Sapareto and W.C. Dewey, “Thermal dose determination in cancer therapy,” Int. J. Radiation Oncol. Biol. Phys., vol. 10, no. 6, pp. 787–800, June 1984.
 H. Rhim, S. N. Goldberg, G. D. Dodd, L. Solbiati, H. K. Lim, M. Tonolini, and O. K. Cho, “Essential techniques for successful radiofrequency thermal ablation of malignant hepatic tumors,” Radiographics. vol. 21, pp. S17–S35, Oct. 2001.
 H. Zhang and D. Liu, Fuzzy Modeling and Fuzzy Control. Boston, MA: Birkhäuser, 2006.
 K. M. Passino and S. Yurkovich, Fuzzy Control, Addison-Wesley, Menlo Park, CA, 1998.
 Z. Kovacic and S. Bogdan, Fuzzy Controller Design: Theory and Applications. Boca Raton, FL: CRC/Taylor & Francis, 2006.
 G. Lindfield and J. Penny, Numerical Methods: Using MATLAB. Ellis Horwood, NY: Academic Press, 2012.
 N. Park, D. Lee, and D. Hyun, “A power-control scheme with constant switching frequency in class-D inverter for induction-heating jar application,” IEEE Trans. Ind. Electron., vol. 54, no. 3, pp. 1252–1260, June 2007.
 B. Samo, B. Jovan, D. Janko, and G. Gregor, “Magnetic effects on thermocouple,” Meas Sci Technol., vol. 25, no. 3, pp. 1–11, Feb. 2014.
 L. Zhen and L. Xu, “Fuzzy learning enhanced speed control of an indirect field-oriented induction machine drive,” IEEE Trans. Control Syst. Technol., vol. 8, no. 2, pp. 270–278, Mar. 2000.
 R. C. Dorf and R. H. Bishop, Modern Control Systems. Upper Saddle River, NJ: Prentice-Hall, 2008.