A Novel Fractional Order Slip Model for Dynamic Friction Estimation in Industrial Robot

Accurate friction modeling remains a critical challenge in high-precision robotic systems due to the complex, nonlinear nature of friction and the distinct behaviors characterizing the presliding and sliding regimes. In this article, a novel fractional-order generalized Maxwell-slip (FO-GMS) model is proposed, incorporating fractional-order elements to describe both elastic and viscous deformations in a unified and compact form. Key properties of the proposed model, including hysteresis, the nondrifting property, the Stribeck effect, and friction lag, are investigated. To efficiently decouple and estimate the static and dynamic parameters, a hybrid identification method combining constrained nonlinear least-squares and a linear decreasing weight particle swarm optimization algorithm is developed. Experimental validation on an industrial robot demonstrates that the FO-GMS model achieves an error reduction of over 50% while utilizing 23.08% fewer parameters compared to conventional GMS and existing fractional models. Furthermore, the sensitivity and generalization capability of the model have been verified across different robot joints and operating velocities. These results confirm the superior accuracy and computational efficiency of the FO-GMS framework.