Satellite five-axis attitude control simulation based on BP neural network sliding mode controller

To deal with the current high demand for the much more complex satellite control system with high precision, the requirements for the control algorithm become more and more critical. This paper targeted at satellite systems with cameras that capture targets. By simplifying the derivation of the Kane equations, Five-axis satellite dynamic model with flexible appendage impacts is drawn, then modeling CMGs to be the implementing agency for this satellite, adding integration module, whole system simulation model is got. This paper studies the optimizing parameters of sliding mode controller by BP neural network for satellite attitude control and the effect and precision can be greatly improved. It is verified that the intelligent algorithm applied to the parameters optimization of traditional controller can be used in satellite attitude control.