Spacecraft attitude control method based on deep neural network approximation MPC
A deep neural network and attitude control technology, applied in attitude control, vehicle position/route/height control, control/adjustment system, etc., can solve problems such as difficult to determine the scale of neural network, unsuitable for spacecraft, difficult training, etc., to achieve The effect of improving online control efficiency, avoiding constraint violations, and improving the time required for training
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[0056] The present invention will be further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.
[0057] Such as figure 1 As shown, the steps of the spacecraft attitude control method based on deep neural network approaching MPC in this embodiment include:
[0058] Step S01. Training data set generation: Configure the MPC controller based on the model predictive control method. The MPC controller inputs the attitude parameters of the spacecraft and outputs the desired control torque for the spacecraft. The attitude parameters include attitude errors or are obtained by converting attitude errors. Parameters, the attitude error is the error value between the current attitude parameter value and the expected attitude parameter value of the spacecraft, the control inputs multiple input attitude parameters into the MPC controller, and obtains corresponding multi...
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