Evaluation of AI-Based Control Systems for Nonlinear Dynamic Processes

This study explores the performance of a neural network-based adaptive control system, aimed at minimizing control errors and optimizing computational efficiency over time. The primary purpose of this research is to examine the dynamic interaction between the system's learning abilities, computational load, and stability, offering insights into how these factors influence overall performance. The method used combines a neural network with a reward-based mechanism to adjust the system’s parameters in response to real-time control errors, enhancing its adaptability. The results of the study indicate significant improvements in system performance as the neural network adapts. Specifically, the control error is reduced steadily over time, from an initial error of approximately 0.8 at t = 0 to a lower value of around 0.2 at t = 10. The reward function mirrors this reduction in error, reaching a peak of -0.64 at the beginning and stabilizing at a value close to -0.04, demonstrating the system's efficient learning process. Computational load increases with time, starting at 10 units at t = 0 and reaching 110 units by t = 10, reflecting the growing complexity of the system as it adapts. Meanwhile, the adaptive gain steadily increases, starting at 1 and reaching approximately 2.5 by the end of the time period, showing the system’s learning capacity. Stability measures and cost functions also reflect this adaptation, with stability reaching a value of around 1.8 at t = 10, while the cumulative cost increases steadily, indicating a trade-off between error reduction and resource consumption. The energy consumption, calculated as a cumulative sum of computational load, rises from 0 at t = 0 to approximately 550 units at t = 10. Overall, the study demonstrates the neural network controller's effectiveness in reducing control errors, with a clear trade-off between performance improvement and computational demands. These findings highlight the importance of optimizing adaptive systems, balancing resource consumption with performance, especially in real-time control applications where efficiency is crucial.