A separable nonlinear model for industrial robot dynamic identification considering installation angles

Dynamic identification of industrial robots is crucial to achieving high-performance control. However, when installed on an unknown tilted surface, the robotic dynamics are nonlinearly influenced by the installation angles. Existing methods use conventional linear regression models to perform identification by coupling installation angles with dynamic parameters, failing to identify them separately. This coupling introduces nonlinearities into the linear parameter set, resulting in dynamic parameters that lack generalizable physical meaning and compromise control accuracy. This paper introduces a novel separable nonlinear model (SNLM) for independently identifying installation angles and dynamic parameters. Simultaneous in-situ calibration of the force/torque (F/T) sensor measuring robot base wrench is also included within the proposed SNLM to improve the identification accuracy. A closed-form method is provided to quickly identify the proposed SNLM, utilizing certain properties of the model. To further enhance the robustness of the solution, an iterative algorithm based on the variable projection (VP) algorithm is suggested. Several simulations and experiments are conducted on two different robots to verify the effectiveness of the proposed algorithms.