4 research outputs found
Bayesian optimization-based parameter tuning of model predictive controllers for shared control and autonomous motion of medical imaging robots
A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers
This paper presents a Bayesian optimization framework for the automatic
tuning of shared controllers which are defined as a Model Predictive Control
(MPC) problem. The proposed framework includes the design of performance
metrics as well as the representation of user inputs for simulation-based
optimization. The framework is applied to the optimization of a shared
controller for an Image Guided Therapy robot. VR-based user experiments confirm
the increase in performance of the automatically tuned MPC shared controller
with respect to a hand-tuned baseline version as well as its generalization
ability
A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers
This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the representation of user inputs for simulation-based optimization. The framework is applied to the optimization of a shared controller for an Image Guided Therapy robot. VR-based user experiments confirm the increase in performance of the automatically tuned MPC shared controller with respect to a hand-tuned baseline version as well as its generalization ability