Online Identification of Environment Hunt–Crossley Models Using Polynomial Linearization

Abstract

Online environment dynamic estimates are often used for the control of robots, telerobots, and haptic systems. The nonlinear Hunt–Crossley (HC) model, which is physically consistent with the behavior of soft objects with limited deformation at a single point of contact, is being increasingly used in robotic control systems. The HC model can be identified online using a single-stage log linearization technique; however, the accuracy and applicability of the existing method is limited. We propose a two-stage polynomial identification method, which uses a quadratic approximation in the first stage to generate a linearly parameterized model of the HC dynamics (Quad-Poly). The coefficients of the Quad-Poly model are then used in the second stage to extract the HC parameters using a lookup table and recursive least squares parameter estimation. The proposed method is experimentally assessed against a previous natural logarithm linearization method, and further tested for time-varying environment dynamics and human-generated trajectories and for robustness against uncertainties in the measured data and system parameters.