Study on the attitude control for High-speed Rotary Missile based on neural networks

In the thesis, a new attitude control methods with neural network is set up for forcasting High-speed Rotary Missile. When considering the fix-errors, output equation is complex and nonlinear. The system is identified with BP neural network based on LM algorithm. Simulation results show that these attitude control methods can implement efficiently precise attitude control of rotary missile and identified effectively. It is possible that the integrated navigation system will be used in theory.