Identification and appropriate parametrization of parallel robot dynamic models by using estimation statistical properties

This paper presents a complete approach for parametrization of model- and knowledge-based controller for parallel robots. By combining and merging methodologies from mechanics, system theory, information processing and intelligent control, an accurate and compact method resulted and is substantiated with experimental results achieved on an innovative hexapod PaLiDA. An appropriate form of excitation trajectories helps to overcome classical identification problems, like disturbances in the acceleration signals. The Gauss-Markov estimator is applied for solving the over determined linear equation system. A novel method is presented that uses statistical and uncertainty attributes of the estimate for choosing an optimal structure and parameter number of the dynamics model.