An adaptive modeling approach based on ESN for robotic belt grinding

Robotic belt grinding system has good prospect to release hand-grinder from their dirty and noisy working environment. However, as a kind of non-rigid processing system, it is a challenge to model its processes precisely for free-form surface because its performance is unstable due to a variety of factors, such as belt wear and belt replacement. In order to adapt to the variability, an adaptive modeling approach based on echo state network (ESN) is presented, whose major idea is to exhaust information from new data by using sliding window technique to select training samples. With machine learning paradigm this approach is more flexible than traditional ones which often base on formula and experimental curves. Experimental results of grinding turbine blades demonstrate this approach is workable and effective.