Neuro-adaptive finite time composite fault tolerant control for attitude control systems of satellites

Abstract

An adaptive finite time composite fault tolerant control strategy based on an optimized neural network for Attitude control systems (ACSs) of satellites is proposed considering the state time-varying delays, concurrent actuator and sensor faults, system uncertainties, modelable external disturbance and operating noise. An uncertain time-varying state space model for ACSs of satellites is established, and sensor faults are equivalent to actuator-like faults. A disturbance observer is designed for estimating the modelable external disturbance, and an improved dwarf mongoose optimization (DMO) algorithm based on the Levy flight distribution is utilized to optimize the basis function of hyperbasis function neural networks to better estimate the augmented actuator faults that include the actuator fault and the actuator-like fault. Furthermore, an adaptive finite time composite fault-tolerant controller is proposed, which includes the delay-dependent feedback control law, disturbance estimation based-disturbance compensation law and the adaptive fault compensation law based on the augmented fault estimation using the improved DMO-hyper basis function neural network. The finite time boundness of the close-loop dynamics to the uncertainties, operating noise, and augmented actuator faults and the robustness of the measurement to the uncertainties, operating noise and augmented actuator faults are analyzed, and the observer and controller design is formulated as the linear matrix inequalities. Simulation examples for ACSs in different working conditions are considered to exhibit the proposed method's effectiveness.