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We present a novel 3D adaptive observer framework for use in thedetermination of subsurface organic tissue temperatures in electrosurgery. Theobserver structure leverages pointwise 2D surface temperature readings obtainedfrom a real-time infrared thermographer for both parameter estimation andtemperature field observation. We introduce a novel approach to decoupledparameter adaptation and estimation, wherein the parameter estimation can runin real-time, while the observer loop runs on a slower time scale. To achievethis, we introduce a novel parameter estimation method known as attention-basednoise-robust averaging, in which surface thermography time series are used todirectly estimate the tissue's diffusivity. Our observer contains a real-timeparameter adaptation component based on this diffusivity adaptation law, aswell as a Luenberger-type corrector based on the sensed surface temperature. Inthis work, we also present a novel model structure adapted to the setting ofrobotic surgery, wherein we model the electrosurgical heat distribution as acompactly supported magnitude- and velocity-controlled heat source involving anew nonlinear input mapping. We demonstrate satisfactory performance of theadaptive observer in simulation, using real-life experimental ex vivo porcinetissue data.

Infinite-Dimensional Adaptive Boundary Observer for Inner-Domain  Temperature Estimation of 3D Electrosurgical Processes using Surface  Thermography Sensing