Data-driven friction modeling and compensation for rotary servo actuators

Abstract

This study proposes a data-driven friction modeling and compensation method aimed at solving the problem of servo performance degradation caused by friction in rotary servo actuators. First, a data-driven friction modeling method is proposed on the basis of the physics-informed neural network (PINN) and the LuGre model. The constructed friction model consists of sliding regime, static regime, and presliding regime, which extends the variables of the friction model to include velocity and position. The data-driven friction model not only retains the accuracy of the LuGre model in describing the dynamic behavior of friction at zero velocity but also improves the accuracy and convergence speed of the model through the powerful learning ability of PINN, which is verified in the two examples of constructing friction test data. Second, on the basis of the data-driven friction model, a composite compensation strategy centered on friction compensation is proposed. The friction compensator is used to compensate the internal friction of the actuator, and the extended Kalman filter is used to suppress the random disturbance to achieve the precise control of the servo actuator. Experimental validation of the proposed compensation strategy against three traditional control methods demonstrates its superiority, with average improvements of 49.5%, 30.4%, and 32.7% in velocity tracking accuracy, respectively, while ensuring consistent accuracy across different positions. The proposed data-driven friction modeling and compensation method provides a new perspective and method for overcoming the effect of friction.

Graphical abstract

Keywords

rotary servo actuator / physics-informed neural network / LuGre model / data-driven friction model / friction compensation

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Higher Education Press