# Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications
## 5 Discussion
Our approach builds upon these findings by integrating neural networks to predict joint torques directly, allowing for faster adaptation and reduced computational complexity in real-time robotic control. Moreover, the integration of the IoT with robotic control systems has been extensively discussed, particularly in smart farming applications where real-time data processing and adaptability are crucial [35; 36; 37]. This study further advances the field by demonstrating how an IoT framework can enhance the efficacy of ML models in dynamically adjusting the control parameters based on real-time sensor inputs [44]. The adaptability of this system is critical for environments requiring constant adjustments due to rapidly changing conditions [42]. Our neural network model demonstrated a high accuracy and low loss, with a final accuracy of 0.9304 and a loss of 0.1850. These results suggest that the model effectively captured the underlying patterns in the training dataset, demonstrating strong potential for real-world application in robotic control systems. However, while this level of accuracy is commendable, there is room for improvement when compared to the results obtained in studies such as that of [PERSON] et al. [53], where a neural network approach integrated with Inertial Measurement Unit (IMU) and Ultra-Wideband (UWB) data fusion achieved a 99% positioning accuracy for moving robots. The combination of nonlinear control methods, machine learning, and IoT technologies creates a robust platform for future research and development. While this study provides a solid foundation, there are several avenues for future work. One area of focus could be improving the scalability of the system for even higher degrees of freedom in robotic platforms. Additionally, exploring more advanced neural network architectures, such as deep reinforcement learning models, could further enhance the system's adaptability and decision-making capabilities in highly uncertain environments. Moreover, future studies could investigate the integration of other emerging technologies, such as edge computing and 5G, to further reduce latency and improve real-time control in IoT environments [54; 55]. The potential to extend this hybrid approach to other industries, such as autonomous transportation or healthcare robotics, offers promising directions for further exploration [56; 57]. Among these factors, model inaccuracies and sensor delays contribute most significantly to the total tracking error. Neural network prediction errors are also influential, but their impact can be minimized with adequate training. Understanding these contributions allows for targeted improvements in model accuracy, network training, and delay management strategies to enhance real-world performance. In an IoT environment, where communication delays can occasionally occur, the control system's inherent robustness--derived from the combination of the robust control term and the adaptive neural network--enables it to tolerate short-term data unavailability or latency. If the delays are persistent, further techniques such as predictive control can be integrated into the system, where the neural network could predict the likely future states based on historical data, thus maintaining continuity in the control response.