Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

A generalized FFT algorithm on transputers

. A generalized algorithm has been derived for the execution of the CooleyTukey FFT algorithm on a distributed memory machine. This algorithm is based on an approach that combines a large number of butterfly operations into one large process per processor. The performance can be predicted from theory. The actual algorithm has been implemented on a transputer array, and the performance of the implementation has been measured for various sizes of the complex input vector. It is shown that the algorithm scales linearly with the number of transputers and the problem size. 1 Introduction<E-106> A commonly used algorithm in scientific engineering is the Fast Fourier Transform[1]. In various fields, such as control theory, system identification, coding theory and signal processing, the Fast Fourier Transform is a valuable tool. Therefore, a lot of effort has been spent in the past in finding efficient implementations of this algorithm. Most of the implementations in the past made use of fast..