High precision tracking control for linear servo system based on intelligent second-order complementary sliding mode

To develop a high-performance permanent magnet linear servo system, a novel intelligent second-order complementary sliding mode control (ISOCSMC) method is proposed in this paper. First, the mathematical model of permanent magnet linear synchronous motor (PMLSM) with uncertainties is established. To conquer the uncertainties and reduce chattering, a second-order complementary sliding mode control (SOCSMC) with fast convergence and global robustness is developed. However, because the value of lumped uncertainty is difficult to obtain in advance and cannot be adjusted automatically, an intelligent control system using recurrent Gegenbauer fuzzy neural network (RGFNN) based on improved whale optimization algorithm (IWOA) is proposed to further improve the servo performance. RGFNN acts as an estimator to approximate the lumped uncertainty, and IWOA is used to convergence of the output tracking error and improve the convergence speed of RGFNN. Finally, the experimental results demonstrate the proposed controller exhibits high tracking accuracy and strong robustness against the parameter variations and load disturbances in comparison with the recently reported control strategies.