A new closed-loop output error method for parameter identification of robot dynamics

Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method requires only the joint force/torque measurement. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. This is a non-linear least-squares problem which is dramatically simplified using the inverse dynamic model to obtain an analytical expression of the simulated force/torque, linear in the parameters. A validation experiment on a 2 degree-of-freedom direct drive robot shows that the new method is efficient.

💡 Research Summary

The paper introduces a novel closed‑loop output‑error (OE) approach for identifying the dynamic parameters of robotic manipulators. Traditional offline identification relies on the inverse dynamics model, which is linear in the unknown parameters. In that framework, joint positions, velocities, accelerations, and torques are sampled while the robot follows excitation trajectories, and a linear least‑squares (LLS) solution is applied. Although widely used, this method demands high‑rate joint position sensing, numerical differentiation or band‑pass filtering to obtain velocities and accelerations, and accurate torque measurement, which together increase hardware complexity and introduce noise‑related biases.

The proposed method eliminates the need for joint position, velocity, and acceleration data. Instead, it treats the measured joint torque (or force) as the system output and builds a closed‑loop simulation that mirrors the real robot’s control law and reference trajectory. The simulation uses the direct dynamics model

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