## 4 Simulation Results
Despite these challenges, neural network controllers serve as a valuable baseline for comparison with other controllers, due to their versatility and widespread use in various control applications. Figure 6 illustrates the path tracking results from both controllers in backward motion. It can be seen that there is identical behavior and good tracking for both controllers. However, when comparing the Evolving Neural Network and the sliding mode controller, several distinguishing features become evident, most notably the NN controllers, recognized for their complexity with high accuracy of tracking with good training. Conversely, the sliding mode controller is renowned for its resilience and robustness against system uncertainties and external disturbances. It can adapt to a range of operating conditions and is proficient at handling nonlinearities, making it highly suitable for systems with intricate dynamics and undefined environments. Sliding mode controllers' capability to maintain system stability and performance in the presence of uncertainties and disturbances renders it a more versatile and reliable control strategy in comparison to the NN controller, particularly in complex, dynamic environments where robustness is pivotal. In the side-by-side comparison shown in Figures 7 and 8, we can observe the error distribution of the Lateral Distance Tracking Error and the Heading Angle Error, respectively, presented in the form of histograms. These histograms provide a visual representation of the frequency distribution of errors, allowing us to easily compare the performance of the Neural Network (NN) controller and the Sliding Mode Control (SMC) controller. Specifically, focusing on the Lateral Distance Tracking Error, the histogram in Figure 7 clearly demonstrates that the NN controller has a tighter and more centralized distribution of errors compared to the SMC controller. This is quantitatively supported by the Root Mean Square Error (RMSE) values, with the NN controller achieving a lower RMSE of  
Figure 5: Several plots illustrate the performance of the controller in tracking the test path. 0.32651, compared to the SMC controller's RMSE of 0.44742. The lower RMSE value and the concentrated distribution of errors in the histogram indicate that the NN controller is more accurate and consistent in tracking lateral distance, successfully minimizing deviations from the desired path. When assessing the Heading Angle Error, both controllers perform similarly. The SMC has a slightly better RMSE at 0.10284, whereas the ENN's RMSE is marginally higher at 0.10403. The difference is minimal, suggesting that, in terms of heading angle tracking, both controllers are nearly equivalent in performance. However, a distinct advantage of the SMC controller is its ease of implementation. While neural networks may require extensive training, parameter tuning, and validation on diverse datasets, SMC controllers are often simpler to design, implement, and validate, especially for systems with known dynamics. This ease of implementation can lead to faster deployment times and reduced complexities, particularly valuable in scenarios where rapid prototyping or deployment is essential. In summary, the NN controller demonstrates superior performance in lateral distance tracking,  
Figure 6: Simulation results illustrating the path tracking results from both controllers in backward motion. Figure 7: Histogram of the lateral error distribution for both SMC and NN. Brown color represents the overlap between SMC and NN. but the simplicity and straightforwardness of implementing the SMC controller make it an attractive choice for certain applications. A predictive path-tracking control design can potentially reduce such error peaks, but it involves substantial computational effort, particularly when the model's inherent nonlinearity complicates the optimization problem.