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

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