Simulation of Satellite Attitude Control Based on BP Neural Network

In satellite attitude control algorithms, PID control is often used in engineering practice due to its simple, effective, stable and reliable characteristics. However, PID parameter tuning requires manual adjustment based on experience or a large number of experiments, which has a low efficiency. BP neural network is an intelligent algorithm based on the gradient descent method in the optimization theory, which can approximate any nonlinear function. This paper first establishes the satellite dynamic model under the ideal rigid body, then combines BP neural network with PID and applies it to satellite attitude control, where BP neural network's error back propagation characteristic is used to autonomously adjust the PID parameters, which improves the efficiency. The simulation shows that the step length has a great impact on the results of the control system, so it is necessary to select an appropriate step length for a specific task.