Identification of a flexible robot manipulator using a linear parameter-varying descriptor state-space structure

This paper presents a new approach for the identification of the dynamical model of flexible manipulators. The structure of the identified model, chosen as a descriptor LPV model, is derived from the original non-linear equations. A set of experiments around different configurations is involved, which is suitable for an accurate measurement of the tip of the manipulator by video camera. The final estimation step is global, allowing the direct identification of the global model based on the collection of local experimental data. In an output error context, a genetic algorithm is used for the minimization of the identification criterion. As a case study, a robotic arm with two flexible segments is considered. Identification results based on simulations including noise show the effectiveness of the approach.