基于RBF神经网络的卫星姿态跟踪自适应控制

Shao-ting Yu, Cai-zhi Fan
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引用次数: 0

摘要

针对具有不确定惯性矩和外界干扰的卫星姿态跟踪问题,提出了一种基于RBF神经网络的自适应控制方法。首先,基于误差四元数和误差角速度,推导了卫星姿态跟踪的运动学和动力学方程;然后,设计了一种基于RBF神经网络的直接控制器,并利用Lyapunov稳定性理论证明了所设计的控制器能够保证卫星姿态跟踪系统的渐进稳定性。最后,利用MATLAB/SIMULINK软件对所设计的控制方法进行了仿真验证。结果表明,基于RBF神经网络的自适应控制能有效克服系统中不确定扰动的影响,提高姿态控制精度,具有较强的鲁棒性。
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Adaptive Control of Satellite Attitude Tracking Based on RBF Neural Network
Aiming at the problem of satellite attitude tracking with uncertain moment of inertia and external interference, an adaptive control method based on RBF neural network is proposed. First, based on the error quaternion and error angular velocity, the kinematics and dynamics equations of satellite attitude tracking are derived. Then, a direct controller based on RBF neural network is designed, and the Lyapunov stability theory is used to prove that the designed controller can ensure the progressive stability of the satellite attitude tracking system. Finally, the simulation of the designed control method was verified by MATLAB/SIMULINK software. The results show that the adaptive control based on RBF neural network can effectively overcome the influence of uncertain disturbances in the system, improve the accuracy of attitude control, and has a strong Robustness.
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