Modeling and control: Theoretical development with application to hyperthermia cancer therapy

This dissertation develops and evaluates the potential efficacy of some of the tools feedback control tools needed to improve clinical hyperthermia treatment of cancer. In particular, this dissertation accomplishes three specific aims: (1) to develop an improved, balanced realization based method of model reduction technique; (2) to generate an exact temperature tracking technique for hyperthermia treatments, using the improved model order reduction technique in conjunction with an inverse dynamics based feedforward/feedback controller; and (3) to extend iterative learning control to include nonminimum phase systems. The iterative learning control technique developed here uses the inverse dynamics method of exact output tracking trajectory generation and the improved balanced realization model reduction method.; This dissertation provides some important steps toward the implementation of advanced control techniques in the hyperthermia clinic. The control algorithms developed in this dissertation have been applied to a clinical system and have been shown to work in an experimental setting. In addition, this dissertation advances the theoretical controls field by extending the theory in both model order reduction and iterative learning control. Finally, algorithms have been developed, coded, and tested experimentally to implement the techniques developed in this dissertation work in a clinical treatment system.