Control methods for robot-based predictive compensation of respiratory motion

Abstract

Abstract Robot-based tracking of spontaneous respiration-triggered motion of human tissue becomes increasingly important in robot-guided imaging, interventional radiology, surgery and radiotherapy. This work provides a first general comparative assessment of the tracking performances that can potentially be achieved by common control schemes under realistic conditions. Cartesian exemplary spontaneous respiratory motions of 185 min duration were recorded from a healthy male subject and transformed into reference trajectories for tracking experiments. Prediction error was modeled by random perturbation of these trajectories with increasing amplitude over the prediction horizon. The controlled system, an industrial robot, was represented by an identified ARX model. A repetitive (RC), adaptive (ALC), feedforward (FFC) and model-based predictive controller (MPC) were implemented. ALC adaptively identifies robot latency and compensates this latency by according time-shift of reference coordinates. Control performance was assessed based on the control error as well as this errors’ estimated worst case dosimetric consequence in a radiotherapy application. Deviations from periodicity in reference trajectories were statistically quantified and reproduced for simulation-based assessment of RC. ALC, FFC and MPC were assessed experimentally. Realistic deviations from periodicity made performance of RC unprofitable, even with ideal function of controller and period-robustness techniques. The same applies potentially to related methods such as iterative learning control. ALC, FFC and MPC performed comparably for ideal prediction. Despite realistic prediction error, FFC and MPC achieved submillimeter control error, significantly outperforming ALC. Thus, FFC or MPC should be employed, RC and ALC should not be employed in robot-based tracking of spontaneous respiration-triggered motion of human tissue.