Model predictive control (MPC) applied to a simplified model, magnetic nanoparticle hyperthermia (MNPH) treatment process

Abstract

Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements the conventional ionizing radiation and chemotherapy. MNPH involves the injection of iron oxide nanoparticles into the tumor and exposure to an alternating magnetic field (AMF). Iron oxide nanoparticles generate heat due to hysteresis loss when exposed to radiofrequency AMF. Exposing human tissue to AMF causes non-specific heating in tissues through induced eddy currents, which must be minimized. A pulse-width-modulated AMF has been shown to minimize eddy current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK® to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] tumor temperature is required for at least 30 [min] while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. The MPC used the AMF amplitude as an input and the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared to the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) compared to PI (115 [s] and 5.7 [%]) responses. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.