Gray-box LPV model identification of a 2-DoF surgical robotic manipulator by using an H∞-norm-based local approach

Abstract

Identifying a linear parameter-varying (LPV) model of a non-linear system from local experiments (i.e., experiments with small displacements around given positions) is a problem which still deserves attention. Rather than building a model either from the law of physics or from experimental data independently the combination of an analytic and an experimental approach is used in this paper to identify an LPV model of a 2-DoF flexible surgical robotic manipulator. This LPV model is more precisely estimated by applying a dedicated Hoo-norm-based technique to yield a final parameter dependent model written as a linear fractional representation (LFR). This contribution demonstrates the effectiveness of the used Hoo-norm-based identification technique by applying real data sequences gathered on a real flexible robotic manipulator.