Stable robot manipulator parameter identification: a closed-loop input error approach

This paper presents an on-line parametric estimation method for robot manipulators. The identification algorithm estimates the parameters by using the input error between the robot and a parallel estimated model. Both, the robot and the estimated model are controlled by two Proportional–Derivative (PD) controller tuned with the same gain values, and a persistent excitation (PE) signal for ensuring parameters convergence is included. The exact model matching and the estimation error cases are analysed. Noisy state measurements and filters are avoided in the model parameterization by using only the states of the estimated model. A second parameter identification algorithm, which is based on a composite update law, is also proposed. It improves parameters convergence and robustness of the update rule in presence of estimation errors. The stability of the closed-loop dynamics related to the estimated model is assessed via Lyapunov stability theory. Simulations are carried out to validate the proposed approaches.