Neural network-based output synchronization control for multi-actuator system

This article proposes a novel output synchronization control strategy for a class of multi-actuator system with strict-feedback form. High-order sliding mode observer is utilized to estimate the system states with the only available output signal. Moreover, radio basis function neural network combined estimated states is applied to handle the system uncertainties, which helps to realize the combination of state observation and disturbance observation and reduce the dependence on the system model. Furthermore, a new synchronization control method is employed to improve the synchronization accuracy of multiple actuators through backstepping technology. Based on the above control strategies, the control performance of the multi-actuator system is greatly enhanced while the design difficulty of the controller is significantly reduced. In the end, simulations and experiments examples are used to illustrate the superiority of the designed technique.