Robot parameter identification via sequential hybrid estimation algorithm

The authors consider the problem of improving the parameter identifiability properties of a robot model and derive a sequential estimation algorithm which substantially simplifies the estimating procedure. The estimation of the invariants (masses, inertias, etc.), which usually requires at most 11n parameters for a robot manipulator with n degrees of freedom, can be performed link by link in a sequential manner by n algorithms of size n _i, where Σn_i is smaller than 11n. Optimization of the robot trajectories seeking to improve parameter identifiability can be simplified. This method enhances the numerical algorithm conditioning and facilitates the selection of a high excited identification sequence, improving the parameter identifiability. The convergence of the estimates to their true values can be obtained provided that the information vector associated with each link is persistently exciting