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LPV modeling and identification of a 2-DOF flexible robotic arm from local experiments using an H∞-based glocal approach

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This chapter presents a series of methodological contributions for the identification of flexible manipulators from experimental data. The goal of this identification procedure consists more precisely in obtaining reliable LPV models written as linear-fractional representations (LFR) for a 2-DOF robotic manipulator having structural flexibilities. The case of a two-segment arm initially designed for cardiac robotized surgery is more specifically considered in simulation. In order to reach this goal, the H-based optimization technique described in Chapter 9 is applied. This methodology is indeed very flexible and allows us to derive models with or without structure. Thus, as far as the model structure is concerned, two different cases are considered in this chapter. First, a specific attention is paid to a fully parameterized LPV-LFR, the parameters of which are estimated on the gathered I/O data sequences exclusively. Second, the prior information derived from the study of the nonlinear equations governing the behavior of the robotic manipulator is used to build the structure of the LPV-LFR and an LPV physically structured state-space form is identified from the same I/O data sequences as those used for the fully parameterized state-space form. This study proves that using the synergy between an analytic and an experimental approach can be really helpful for the identification of an LPV flexible robotic manipulator model.

Chapter Contents:

• Abstract
• 16.1 Introduction
• 16.2 Modeling of a flexible robotic manipulator
• 16.2.1 Description of the 2-DOF robotic manipulator
• 16.2.2 Linear fractional LPV representation: a reminder
• 16.2.3 Nonlinear and linearized dynamic models
• 16.3 Identification results
• 16.4 Conclusions
• References

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