An Adaptive Torque Observer Based on Fuzzy Inference for Flexible Joint Application

Abstract

Torque observation techniques have been widely employed to estimate the load torque of flexible joints driven by a permanent magnet synchronous machine (PMSM). However, the performance of the observer degrades significantly when the position and orientation of the robot continuously changes, resulting in substantial irregular load variations. In this paper, an adaptive torque observer based on fuzzy inference is proposed to overcome this issue. Instead of relying on theoretical or numerical derivation, the relationship between the load inertia and the closed-loop poles of the torque observer is expressed by fuzzy inference. This approach enables the flexible configuration of the poles based on the load inertia, allowing for automatic tuning of the gain matrix. Consequently, the observer can ensure robustness and maintain superior performance under varying load conditions. The effectiveness of the proposed observer is validated through simulation and experimental results. It shows that compared to the classical Luenberger observer, the proposed adaptive torque observer can achieve more accurate observation results and exhibits a more dynamic response in the presence of varying load inertia.

Keywords

• adaptive torque observer
• fuzzy inference
• Luenberger observer
• online inertia identification
• flexible joint