Adaptive Control of Motor-Position-Controlled Flexible Joint Robots with Uncertain Joint Stiffness

2026-07-15

AI Summary

This work addresses the challenge of uncertain joint stiffness in flexible-joint robots, which arises from time-varying and aging characteristics of elastic components and significantly degrades model-based control performance. To overcome this issue, a novel adaptive control method is proposed that continuously estimates and updates the nonlinear torque–deflection relationship of each joint online. By integrating an implicit control law with an input-dependent regressor matrix, the approach transcends the conventional limitations of adaptive control frameworks designed for non-elastic systems. The method substantially enhances robustness and tracking accuracy for position-controlled flexible-joint robots under varying stiffness and motor positioning errors. Experimental validation on a platform exhibiting nonlinear stiffness characteristics confirms the efficacy of the proposed strategy.

Abstract

Model-based control of flexible joint robots with position-controlled actuators relies on accurate knowledge of the joint compliance. In practice, precise stiffness models are often unavailable as the properties of physical elastic elements vary with operating conditions and slowly change over time due to wear and aging. To improve model-based control of these systems, we propose an adaptive control approach in this work, which updates an estimate of the uncertain, nonlinear torque-deflection relation of each joint. As opposed to classical adaptive control approaches for non-elastic robots, we rely on an implicit control law and a control-input-dependent regressor matrix to account for the uncertain joint stiffness. We analyze robustness of the approach against errors induced by the motor position controller. Experimental results on a flexible joint with nonlinear stiffness characteristics demonstrate the effectiveness of the proposed approach.

Problem

• flexible joint robots
• uncertain joint stiffness
• position-controlled actuators
• model-based control
• adaptive control

Innovation

• adaptive control
• flexible joint robots
• uncertain stiffness
• implicit control law
• input-dependent regressor