Sliding mode control of Cyber-Physical Systems under limited communication

In this paper, the tracking control problem of uncertain nonlinear robot systems with limited communication and input saturation is studied. Different from the traditional sampling mechanism, a sampling mechanism based on event triggering condition of measurement error is proposed. The radial basis function neural network (RBFNN) is used to solve the uncertainty and input saturation problems of system parameters, and the extended state observer (ESO) is used to estimate the external disturbance. The effectiveness of the proposed control strategy is verified by the numerical simulation results of the manipulator.