# Data-Driven Control Algorithm for Snake Manipulator
## 5 Summary and Prospects  
In this study, the DRL algorithm was applied to a snake manipulator to verify whether it can improve its control accuracy. Through the study of the theory of DRL and the continuity of the action of the snake manipulator, the DDPG method is proposed as a means of designing the controller of the manipulator's joint. A model-based DDPG control method is proposed to verify the effectiveness and superiority of the DDPG controller through the control of two joints, the simulation object environment and the network structure of the agent are established. The agent was trained, and the simulation without disturbance and torque disturbance was carried out. The simulation results verify the feasibility of the method. However, through the modeling of the 2-link snake manipulator, it was found that it is difficult to establish the entire manipulator model. It is difficult to realize the model-based DDPG control method for the whole manipulator control system. In order to solve the control problem of an unknown model of the snake manipulator, a data-driven control method based on DDPG is proposed. It does not need to establish an accurate manipulator model and avoids the influence of the accuracy error of the manipulator model. The training data were established according to the expected trajectory and the actual output trajectory of the traditional control method. Compared with the traditional control algorithm, it is proven that the data-driven control system based on DDPG has higher accuracy and better stability. At present, the control system of the snake manipulator is only based on data theory. In order to establish a complete snake manipulator motion library for training, the actual manipulator motion data and a large number of trajectory data are needed. The trained control system has a good ability to control known movements. For unknown actions, it also achieves a better control effect. By utilizing continuous learning, the strong adaptability of the snake manipulator control system can be realized.  
Conceptualization, K.H. and L.T.; methodology, K.H.; software, L.T.; validation, L.T., C.W., L.W.and Q.Z.; formal analysis, L.T., L.W., and M.X.; investigation, L.T. and G.Q.; resources, K.H., Q.Z.; data curation, K.H.; writing--original draft preparation, L.T., C.W., and L.W.; writing--review and editing, L.T., C.W., and L.W.; visualization, L.T., C.W., and L.W.; supervision, K.H.; project administration, K.H.; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
Research in this article is supported by the National Natural Science Foundation of Chinese (42075130, 61773219, and 61701244), the key special project of the National Key R&D Program (2018 YFC1405703), and I would like to express my heartfelt thanks. I would like to express my heartfelt thanks to the reviewers who submitted valuable revisions to this article.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Original** & **1 st Group** & **2 nd Group** & **3 rd Group** \\ \hline Average & 0.0034 & 0.0301 & 0.0271 & 0.0061 \\ MSE (\(10^{-5}\)) & 0.284 & 4.7225 & 0.8238 & 0.1156 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of simulation training error.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study due to the data being provided publicly.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data and code used to support the findings of this study are available from the corresponding author upon request ([EMAIL_ADDRESS]).  
**Conflicts of Interest:** The authors declare no conflicts of interest.