# Application of Improved Sliding Mode and Artificial Neural Networks in Robot Control
## 4 Discussion and Conclusions  
This study has successfully demonstrated the effectiveness of combining improved sliding surfaces with artificial neural networks in mobile robot control, particularly in precise trajectory tracking. The introduction of an enhanced sliding surface, along with exponential and hyperbolic tangent approach laws, has significantly reduced the chattering phenomenon, commonly encountered in sliding mode control. This improvement is crucial, as chattering can decrease the performance of the control system and lead to increased wear and tear on mechanical components. The use of Radial Basis Function (RBF) neural networks to estimate the nonlinear components of the sliding control law marks a substantial improvement over traditional methods. This approach allows for real-time updates and adaptability to dynamic changes in the environment or operational parameters of the robot, representing a significant advancement in the development of autonomous systems. The real-time learning and adjustment capabilities of the neural network enhance the robustness and flexibility of the control system, making it suitable for complex and unpredictable environments. Simulation results obtained from MATLAB/Simulink confirm the theoretical predictions and demonstrate the practical feasibility of the proposed control strategy. The reduction in the rise and settling times, along with minimal overshoot and steady-state errors, emphasize the precision that this hybrid control strategy can achieve. Moreover, the observed low amplitude and frequency of chattering during the simulations further affirm the effectiveness of the improved sliding surface in minimizing this undesirable phenomenon. In the research on mobile robot control based on neural networks (NN), a prominent issue is the large computational resource requirement due to the complexity and the continuous data processing needed for adaptation and learning. This study applied a combination of neural networks with improved sliding surfaces to reduce chattering and increase accuracy in trajectory tracking. However, real-time computation still demands a substantial amount of computational resources, posing a significant challenge in practical applications. To address this issue, several low-computation control methods have been developed and tested. Notably, adaptive finite-time fuzzy attitude control based on observers for quadrotor UAVs. Both methods use observers to reduce computational load, thereby maintaining high performance without requiring excessive resources. Additionally, finite-time neural control for nonlinear systems with actuator saturation compensation has proven to be effective in handling complex nonlinear systems. Combining NN with nonlinear control laws and saturation compensation allows the system to quickly adapt to environmental changes while reducing computational resource requirements. The results from this study by the authors show that integrating improved sliding surfaces and neural networks can significantly reduce chattering and increase trajectory tracking accuracy, although the neural network model and real-time learning computation still require considerable resources. Low-computation control methods, such as adaptive fuzzy control and the use of observers, have demonstrated their effectiveness in reducing resource requirements while ensuring system performance. Based on these results, this study suggests optimizing neural network architectures to minimize necessary computational resources, testing the system in various real-world conditions to determine its limitations and potential improvements, and integrating low-computation control methods with NN-based control systems to enhance adaptability and reduce required resources. These recommendations will help expand the application of intelligent control systems in practice, ensuring high performance and autonomous operation in various environments. However, implementing such advanced control systems is not without challenges.