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This paper introduces an adaptive control strategy for flexible joint robots that addresses the challenge of uncertain joint stiffness, which can vary due to operational conditions and aging. By employing an implicit control law and a control-input-dependent regressor matrix, the method effectively updates the torque-deflection relationship for each joint, enhancing model-based control. Experimental results validate the robustness and effectiveness of this approach, showcasing significant improvements in performance compared to traditional methods.
Uncertainty in joint stiffness can be effectively managed, leading to more reliable control of flexible joint robots in real-world applications.
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.