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Motion control algorithm and tuning rules for mechanical devices with low sampling-rate electronics

Gil Jorge Juan, Diaz Inaki

Archives of Control Sciences

2020

Vol. 30, No. 1

123--138

Controlling mechanical systems with position and velocity cascade loops is one of the most effective methods to operate this type of systems. However, when using low-rate sampling electronics, the implementation is not trivial and the resulting performance can be poor. This paper proposes effective tuning rules that only require establishing the bandwidth of the inner velocity loop and an estimation of the inertia of the mechanism. Since discrete-time mechatronic systems can also exhibit unstable behavior, several stability conditions are also derived. By using the proposed methodology, a P-PI control algorithm is developed for a desktop haptic device, obtaining good experimental performance with low sampling-rate electronics.

Inferring material properties in robotic bone drilling processes

Gil Jorge Juan, Díaz Iñaki, Accini Fernando

Acta of Bioengineering and Biomechanics

2019

Vol. 21, no. 3

109--118

Recent innovations in robotics have enabled the development of automatic bone drilling tools which allows surgeons to improve the precision of their surgical operations. However, these tools still lack valuable tactile information about the material properties of the bone, preventing surgeons from making decisions while operating. The aim of this work is to explore whether robotic drilling tools can infer bone condition on the basis of certain key measures, particularly thrust force. Methods: To infer material properties in robotic bone drilling processes 1) a complete database of experimental operations with an automatic bone drilling tool is implemented and 2) binary logistic regression models are developed to estimate the type of material from the observed values (mainly the central tendency of the thrust force). This work compares three different materials: bovine bone specimens, porcine bone specimens and FullCure 720, which is a general-purpose resin with, a priori, much less feed resistance. The DRIBON automatic bone drilling tool developed at CEIT is used for the experiments. Results: The classification matrices derived using the logistic models show that it is possible to recognize a bovine bone vs. a porcine bone with a relatively high success rate rate (approximately 90%). In contrast, it is possible to recognize bone material vs. another material (in our case a resin) with a 100% of success. These results are successfully implemented in a new hand-held version of DRIBON. Conclusions: We propose a method and devise a novel hand-held tool which show that robotic systems can effectively infer bone material properties.