A neural network approach for generating derivative information using quantized robot position measurements

In this paper we propose a generalized methodology for determining first and higher order derivatives of quantized measurements obtained using only position sensors. Our goal is to achieve this objective without use of extra hardware sensors, and at the same time to filter out the noise arising from quantization. We accomplish this using time delay neural networks (TDNN) and compare the performance of this scheme with that obtained using linear filtering techniques. The simulation results show the superiority of the proposed TDNN scheme over the linear filtering approach