## 3 Results and Analysis
### Simulation Test of Adaptive Control Method Based on Deviation Pattern Identification  
#### 3.2.1 Experimental Design  
1. Test conditions Forward velocity \(v=0.8\) m/s, initial heading deviation 0\({}^{\circ}\), initial position deviation 0 m, add positioning noise and steering noise, simulation duration 90 s, sampling time 0.05 s, straight path tracking. Three cycles are set up to induce different degrees of oscillation in the 7 th-10 th s of each cycle and different degrees of sideslip in the 25 th-26 th s.
2. Control strategy In practical applications and simulation tests, navigation control deviations are transient, but their subsequent effects on the system are persistent. In order to effectively suppress the effect of transient deviation on the system, the modulation action time is extended after the activation of the sub-regulator according to the degree of oscillation or sideslip, with the logic as shown in (31). \[\begin{array}{l}if\ \textbf{{NavState}}==\textbf{{Osc}}\\ \  
The results of the path tracking curve for each time segment are shown in Table 16.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Maximum Absolute**} & \multicolumn{3}{c}{**Mean Absolute Lateral**} & \multicolumn{3}{c}{**Root Mean Square Lateral**} \\  & \multicolumn{3}{c}{**Lateral Deviation/m**} & \multicolumn{3}{c}{**Deviation/m**} & \multicolumn{3}{c}{**Deviation/m**} \\ \cline{2-10}  & **0-30 s** & **30–60 s** & **60–90 s** & **0-30 s** & **30–60 s** & **60–90 s** & **0–30 s** & **30–60 s** & **60–90 s** \\ \hline Static preview & 0.0642 & 0.117 & 0.192 & 0.0121 & 0.0232 & 0.0271 & 0.0189 & 0.0346 & 0.0476 \\ \multicolumn{10}{l}{Adaptive preview} \\ \multicolumn{10}{l}{distance} & 0.0660 & 0.0713 & 0.115 & 0.0114 & 0.0146 & 0.0158 & 0.0178 & 0.0205 & 0.0265 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Lateral deviation of test.  
Figure 14: Comparison of path-tracking states: (**a**) 0–30 s; (**b**) 30–60 s; (**c**) 60–90 s.  
From the tracking curves, it can be seen that when the system tracking control deviation occurs, the dynamic preview distance control strategy can quickly and effectively inhibit the system oscillation and sideslip deviation, whereas the static preview distance control strategy is more ineffective in inhibiting the deviation, and it takes a longer time for the system to regain the stable state.  
From the lateral deviation data, it can be seen that in the test scenarios of mild deviation (0-30 s), moderate deviation (30-60 s), and severe deviation (60-90 s), the control strategy with adaptive preview distance, compared with static preview distance, has a maximum absolute lateral deviation, MAE and RMSE that are all lower, which fully demonstrates that the former can significantly improve the stability, responsiveness and anti-disturbance ability of the rice transplanter navigation control and the regulation ability in the face of disturbing situations.  
As verified by the above experiments, the control method proposed in this study can significantly inhibit the oscillations of the rice transplanter navigation system due to sideslip and multi-factor error coupling, and the design method of the controller is reasonable.