卫星编队飞行的神经网络滑模控制(英文)

Neural Network-Based Sliding Mode Control for Satellite Formation Flying

该文推导卫星编队飞行的一般相对运动动力学模型,研究将指数趋近律滑模控制与神经网络控制相结合的卫星编队飞行控制方法,设计一种径向基神经网络参数调节器.实时调节指数趋近律的参数,从而取得滑动面的趋近速度和燃料消耗的最优平衡.采用指数趋近律滑模控制法,用饱和函数代替可能产生高频切换信号的开关函数,有效地削弱了滑模控制的抖动.二阶滑模控制结构保证了卫星编队的高精度控制.仿真结果表明了这一控制方法的有效性.

The general dynamical model of satellite formation flying is derived. A sliding mode control method based on neural networks is proposed to improve control accuracy and robustness of satellite formation flying. A neural network based on radial basis function is designed to modify the parameters of exponent reaching law in order to get an optimal balance between convergence speed of the sliding quantity and fuel consumption. Exponent reaching law with saturation function is used to weaken chattering actuated by un-modeled dynamics and high frequency switching control. The second order sliding quantity of the relative position error is used to improve the control accuracy. Simulation results show effectiveness of the neural network-based sliding mode control method.