Flexible satellite neural network backstepping sliding mode attitude control method
A flexible satellite and backstepping sliding mode technology, which is applied in attitude control and other directions, can solve the problems of antenna rotation disturbance, steady-state accuracy and stability, and needs to be improved.
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specific Embodiment approach 1
[0047] Specific embodiment one: a kind of flexible satellite neural network backstepping sliding mode attitude control method, comprises the following steps:
[0048] Step 1: Establish a flexible satellite attitude dynamic model:
[0049] The attitude dynamics model of the flexible satellite is established by using the hybrid coordinate method, and the dynamic equation containing two sailboards and a moving antenna has the following form:
[0050] I s ω · s + ω s × I s ω s + Σ ...
specific Embodiment approach 2
[0070] Specific implementation mode two: the specific implementation process of step 2 described in this implementation mode is as follows:
[0071] Using Euler angles to describe the satellite attitude, and considering the X-Y-Z sequence, the corresponding rotation attitude angles are the satellite attitude roll angles Satellite attitude pitch angle θ and satellite attitude yaw angle ψ, when the satellite is in inertial orientation flight, ω s Expressed as
[0072]
[0073] From the above formula, the satellite attitude kinematics equation is
[0074]
[0075] Figure 5 is a schematic diagram of a satellite structure with a moving antenna; Figure 5 Shown, where the coordinate system OX b Y b Z b is the satellite body coordinate system, OX a1 Y a1 Z a1 is the antenna support arm coordinate system, OX a Y a Z a is the coordinate system of the antenna body; assuming that the antenna is installed in the negative direction of the yaw axis of the satellite body,...
specific Embodiment approach 3
[0100] Specific implementation mode three: the specific implementation process of step 3 described in this implementation mode is as follows:
[0101] Step 3.1, set tracking error z 1 =x d -x 1 ; x d is the reference input, x d When it is 0, there is z 1 =-x 1 ;
[0102] Virtual control amount z 2 = z · 1 + c 1 z 1 = - x 2 + c 1 z 1 , but z · 1 = z 2 - c 1 z 1 , where c 1 is the parameter to be designed, c 1 >0;
[0103] Take the Lya...
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