# Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network
## 4 Experiment and Analysis
### The Analysis of Simulation Performance  
The kinematics model of a two-wheel vehicle is taken as the control object to validate the control performance of this algorithm in the simulation platform. The wheelbase of the vehicle is 1 m, the initial velocity is 0 m/s, the vehicle's nose direction is X-axis positive direction and the initial position is (0,\(-\)1), so the initial position deviation is 1 m. The simulation experiments are carried out under the conditions of the straight path and the multiple curvature path.  
When tracking the straight path, the simulation performance is shown in Figure 6a. Four methods, namely the proposed method, the traditional pure pursuit method, the improved pure pursuit method and backstepping control method [31], are adopted to evaluate the tracking performances and are compared as well. The foresight distance and speed are set as constant values 0.7 m and 0.5 m/s in the traditional pure pursuit method. In the improved pure pursuit [25] method, the foresight distance is changed from constant to dynamic adjustment, which is a linear function of the vehicle velocity. In the backstepping control method, the desired configuration is the origin of the state space, while the initial configuration at t = 0 is x(0) = 0, y(0) = -1, \(\theta\)(0) = 0, \(\varphi\)(0) = 0. We can intuitively see the difference among the above four controllers in Figure 6a, the proposed algorithm takes precedence over the other algorithms in tracking the pre-defined path points as it possesses the rapidity in the initial phase. The lateral error on the straight path in Figure 6b also shows that the proposed intelligent controller has lower lateral error when tracking some straight path points, followed by the improved pure pursuit method, while the traditional pure pursuit method and backstepping control method have the biggest lateral error. However, all four methods can perform well tracking abilities after reaching steady state.  
When tracking the path with multiple curvature, the simulation performance is shown in Figure 7a and lateral error is shown in Figure 7b. The chief reason for choosing this path is that the controller's navigation ability can be tested in various turning operations. As can be seen from Figure 7a, the traditional pure pursuit algorithm can track the expected path after a period of oscillations in the initial stage, but the algorithm presented in this paper can quickly stabilize and show significant advantages. The errors of the traditional pure pursuit algorithm are large on the corners and the vehicle needs to be adjusted over and over again to track the expected path after turning. Although the improved pure pursuit algorithm expresses less deviation than the traditional pure pursuit algorithm at the turning points, it remains worse than the proposed algorithm. However, due to the existence of path curvature, the backstepping algorithm has the largest error in the tracking process. At corners, the proposed algorithm can adapt to the change of curvature quickly, and shows flexible adaptive ability to trace the expected path with tiny errors. In addition, the larger change rate of path curvature,the greater tracking errors of the four algorithms, but the proposed algorithm always performs a superior performance over other algorithms, and its maximum lateral error is 0.08 m after stabilization.  
The above simulation results explain that the proposed method has better control performances and effects in ideal status without noise points. However, in order to verify the performance of this method, it is not enough without real vehicle experiments. Therefore, the field experimental result on a real cleaning vehicle are given below.