## 4 Discussion  
In this paper, we proposed using a sun vector to calculate the ideal turntable position value. Then we took the ideal position value and turntable speed, current, and solar position error signal as date features and used the CNN-LSTM model to realize predicting the trajectory of the pitch axis. The model could adjust parameters adaptively on the training set and had a better performance on the test set.  
The premise of the conventional feedback method is the need to calibrate the optical axis in relation to the instrument body coordinate system and the satellite coordinate system. The position of the pitch axis is then derived by determining the coefficients of the controller. The disadvantage is that certain parameters may need to be adjusted after a long period of system operation. The advantage is the high reliability due to the rigorous model derivation. The advantage of the deep learning method for calculating the pitch axis position is that it can be used without coordinate system calibration, allowing adaptive adjustment of the parameters. However, due to its black-box nature, this research is still at a preliminary stage for on-orbit applications.  
Compared with the trajectory prediction method mentioned above, the pitch axis trajectory prediction of a two-dimensional turntable is a typical time series prediction problem [18; 19]. It is not only related to the previous running state of the turntable but also is affected by the running state of a period of the time window. From this point of view, HMM and an extended Kalman filter only predict the next state through the last state, and the results will affect the accuracy of the prediction [20]. We did not use the equation of motion either, thereby avoiding uncertainty error analysis. We used a deep learning model to predict the trajectory by extracting and learning data features. We used a sliding time window instead of just the last state, and our findings were in accordance with recent studies indicating that a prediction model based on deep learning can achieve satisfactory results [21; 22; 23; 24; 25]. The prediction model based on the LSTM model could effectively avoid gradient disappearance or gradient explosion. In the process of debugging parameters, we also found that the initial value of parameters affected the final performance of the model. The size of the dataset also limited our choice of models. This model had a better performance on a small data set. For the comparison of the performance indicators of the LSTM model and CNN model alone, we could see that the sun vector combined with CNN-LSTM model had a better performance.  
The major limitation of the present study was that the model contained part of the black box, so the model lacked interpretability. At the present stage, we mainly used the calculated results to compare with the real values and then used the comparison results to reverse-extrapolate to achieve the purpose of optimizing the performance of the model. The generalization ability of the CNN-LSTM model has yet to be verified. In addition, when  
Figure 13: Loss diagram of each model for the test set.  
the sliding time window becomes larger, the calculation time of the model will become longer. It will affect the execution time of the model.  
Despite its limitations, the model of CNN-LSTM had a significant short-term prediction effect on turntable trajectory prediction.  
Future applications in orbit will be different from the ground simulation. For the hardware environment, we must choose the processor to meet the onboard radiation--a hardened and tolerant processor generally using an ARM and field-programmable gate array (FPGA) fabric for real-time processing [27]. Software algorithms will be transplanted and optimized accordingly, and the trained network structure needs to be arranged on the onboard platform to work.