Design of a Pose and Force Controller for a Robotized Ultrasonic Probe Based on Neural Networks and Stochastic Gradient Approximation

In medicine and engineering, the implementation of a diagnostic test using an ultrasonic sensor requires suitable contact conditions, and a correct pose to attain the best signal transmission settings. A soft sensor probe provides a good surface adaptation and forces transfer, but it introduces nonlinearities and noisy measurements, making it difficult to control the probe during a real time test by conventional algorithms. In this work, a data driven controller is developed to control force and pose of a soft contact ultrasound sensor. The adaptive controller is based on a fuzzy-rules emulated network structure with the learning algorithm using a stochastic gradient approximation. The proposed control algorithm overcomes the noise environment conditions and nonlinearities of the unknown nonlinear discrete-time system. This was numerically validated and then, experimentally tested with an industrial robotic system using an ultrasonic probe designed in our lab. The results show that the proposed controller performs well under the contact-force regulation and can find the correct contact orientation with a fast convergence.