# Manipulator Smooth Control Method Based on LSTM-XGboost and Its Optimization Model Construction
## 6 Conclusions  
In this study, by analyzing the relationship between the command parameters of the robotic arm and the motion stability, the command-feature index and feature index-stability score prediction models are established by using XGBoost and weight-sharing LSTM models to predict the motion performance and stability of the robotic arm with high accuracy so as to meet its operational stability and time optimality requirements under the ever-increasing motion performance requirements and labor cost pressures. The main conclusions are as follows:  
1. By using the six-axis Bluetooth attitude angle measurement sensor to collect the vibration signal of the manipulator, and using the XGboost model and the weight-sharing LSTM model, the command-characteristic index prediction model and the characteristic index-stability scoring model are established. These models can predict the acceleration, angular velocity and angle on the XYZ axis during the movement, as well as the overall performance indicators of the robotic arm, such as jerk. The performance on training and test sets is good, the mean absolute error (MAE) is low, and the prediction accuracy is high.
2. The model can handle complete sample motions containing multiple actions and multiple instructions. According to the instruction combination sequence, the model can predict the immediacy index of the sample, and the consistency is good. In addition, according to the predicted stability index, the model can convert and predict the predicted scores that are roughly consistent with the sample score card scores, although there is an absolute average score error caused by the model error.
3. With the increase of labor costs year by year, the mechanical arm has replaced the repeated labor of people in more and more positions, but this also puts forward higher requirements for the motion performance of the mechanical arm. Operation stability and time optimality are two of the most important requirements for the motion performance of manipulators. Researchers usually calculate the maximum velocity curve that satisfies the acceleration constraint by transforming the operational stability requirement into the acceleration constraint so as to obtain the time-optimal trajectory of the manipulator with the operational stability constraint. The above model shows good performance in this aspect and can predict the corresponding stability score according to the set parameters so as to effectively optimize the operation performance and stability of the manipulator.  
Methodology, S.Y. and Y.S.; Software, S.Y.; Writing--original draft, S.Y.; Writing--review & editing, Y.S.; Visualization, S.Y.; Supervision, Y.S.; Project administration, Y.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable for studies not involving humans or animals.  
**Data Availability Statement:** No new data is created.  
**Conflicts of Interest:** We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.