Recursive adaptive integral sliding mode control based on extended state observer for dual-motor servo system

This paper presents a novel recursive adaptive integral sliding mode (RAISM) control method based on extended state observer (ESO) for the class of dual-motor nonlinear servo system with unknown backlash. The ESO is utilized to estimate the useful states and predigest the designing course. Then, applying the Chebyshev neural network, the RAISM controller is designed to effectively eliminate the reaching phase and avoid the problem of singularity. The simulation results of comparative experiments verify the validity and higher tracking accuracy of the proposed method.