A COMPARISON OF REDUCED-ORDER MODELING TECHNIQUES FOR APPLICATION IN HYPERTHERMIA CONTROL AND ESTIMATION

Reduced-order modelling techniques can make important contributions in the control and state estimation of large systems. In hyperthermia, r educed-order modelling can provide a useful tool by which a large ther mal model can be reduced to the most significant subset of its full-or der modes, making real-time control and estimation possible. Two such reduction methods, one based on modal decomposition and the other on b alanced realization, are compared in the context of simulated hyperthe rmia heat transfer problems. The results show that the modal decomposi tion reduction method has three significant advantages over that of ba lanced realization. First, modal decomposition reduced models result i n less error, when compared to the full-order model, than balanced rea lization reduced models of similar order in problems with low or moder ate advective heat transfer. Second, because the balanced realization based methods require a priori knowledge of the sensor and actuator pl acements, the reduced-order model is not robust to changes in sensor o r actuator locations, a limitation not present in modal decomposition. Third, the modal decomposition transformation is less demanding compu tationally. On the other hand, in thermal problems dominated by advect ive heat transfer, numerical instabilities make modal decomposition ba sed reduction problematic. Modal decomposition methods are therefore r ecommended for reduction of models in which advection is not dominant and research continues into methods to render balanced realization bas ed reduction more suitable for real-time clinical hyperthermia control and estimation.