Extended nonlinear time-varying lugre-based friction model identification of robot manipulator with sliding mode control compensation approach

In this article, the values of torque loss due to the friction phenomenon in the robot joints are initially identified; then, by considering the lugre friction model and applying the nonlinear least-squares error-estimation method, a model for this phenomenon is presented. To make the studies more efficient, the general lugre friction model has been used in three forms for modeling the system's friction that the first model was the general lugre model, and in the other two Extended models, the parameters are considered as time variables that are estimated online. It should be noted that the third model includes the nonlinear viscous term too. Then, by using the nonlinear sliding mode controller, which is a robust control against possible uncertainties in the system, the performance of the designed algorithms is examined. The results show the superiority of the identified models with time-varying parameters over the model with constant parameters. Finally, by the use of represented friction models, a more accurate model for this system has been introduced. Eventually, the results of the simulations have been tested on a 6R laboratory robot to prove the validation of the results with the proposed algorithm, and as it showed, the final friction model has performed better.

Automated summary

In this article, the values of torque loss due to friction in robot joints were identified, and a model for this phenomenon was presented using the lugre friction model and nonlinear least-squares error-estimation method. Three forms of the lugre friction model were used to model the system's friction, with the third model including a nonlinear viscous term. The performance of the designed algorithms was examined using a nonlinear sliding mode controller. The results showed the superiority of the identified models with time-varying parameters. The accuracy of the proposed friction models was validated through simulations on a 6R laboratory robot.