A Robust Sliding Mode Control for Electro-Optical Stabilized Servo Tracking System Based on Extended State Observer and Adaptive Neural Network

The tracking performance and line-of-sight stabilization of electro-optical stabilized servo tracking systems are often affected by various disturbances and uncertainties. To address the problems, a robust motion control scheme is developed. The control strategy effectively amalgamates sliding mode control (SMC), extended state observer (ESO), and adaptive radial basis function (RBF) neural network. Specifically, an ESO is constructed to estimate unknown disturbances and uncertainties in the system. An RBF neural network is employed to correct the estimation accuracy of ESO for time-varying disturbances. Furthermore, the sliding model control strategy is combined with ESO and RBF neural network to mitigate the impact of multi-source disturbances and uncertainties on control precision. The asymptotic stability of the system is demonstrated through Lyapunov theory and LaSalle invariance principle. Finally, simulations are implemented to illustrate the superiority and effectiveness of the proposed control method compared to the traditional SMC-ESO method.