Neural network–based active control of a rigid–flexible spacecraft with bounded input

Abstract

This paper investigates the simultaneous control of a spacecraft with a rigid central hub and two flexible appendages. Taking into account the coupling between the vibration of flexible appendages and the motion of the hub, the rigid–flexible spacecraft subjected to external disturbance is modeled as a hybrid lumped–distributed parameter system. To attenuate the vibrations of the hub and flexible appendages and simultaneously track the given attitude angle, two boundary control laws are developed based on the original dynamic equation, where all system modes are controlled and spillover instability can be avoided. Using the active disturbance rejection control technique, two extended state observers are designed to obtain the estimation of external disturbances, which can be canceled in the negative feedback loop. The radial basis function neural network with adaptive weight law is employed to deal with the impact of input saturation. The well-posedness of the closed-loop system is proved by applying the semigroup theory. Under the rigorous Lyapunov analysis, the proposed control strategy ensures the practical stability of the rigid–flexible spacecraft system. Simulation verifies the performance of the designed control scheme.