## 4 Conclusions  
This paper presents a novel hybrid approach to control system design that successfully integrates traditional PID control methods with modern artificial intelligence and machine learning techniques. Our proposed method combines LSTM-based real-time model identification with Genetic Algorithm optimization, demonstrating significant improvements in both control performance and computational efficiency.  
The key contributions of this research can be categorized into four main aspects. First, in terms of model reduction, our hybrid method demonstrates superior accuracy through the synergistic combination of numerical calculations, Genetic Algorithms, and LSTM networks. The proposed approach achieves perfect matching between higher-order and reduced models in steady-state response, showing a 28.5% improvement in model accuracy compared to [PERSON]'s method.  
Second, the enhanced control performance is evidenced by substantial improvements across multiple metrics. The system achieves a 13.4% improvement in IAE, 12.3% improvement in ISE, and 15.0% improvement in ITAE. Furthermore, the dynamic response characteristics show a 43.1% reduction in overshoot and 24.5% faster settling time, along with superior disturbance rejection capabilities compared to conventional methods.  
Third, our hybrid approach demonstrates remarkable computational efficiency. The implementation results show a 40% reduction in execution time, a 60% reduction in memory usage, and a 35% reduction in CPU utilization. These improvements are achieved while maintaining real-time adaptation capability with minimal computational overhead, making the system highly practical for industrial applications.  
Fourth, the system exhibits enhanced robustness characteristics, particularly in terms of adaptability to parameter variations and disturbance rejection. The combination of LSTM-based identification and Genetic Algorithm optimization enables better handling of system uncertainties, resulting in more stable operation across various operating conditions.  
These results demonstrate that the integration of traditional control theory with modern AI/ML techniques can lead to significant improvements in control system performance. The proposed hybrid approach represents a promising direction for the future development of industrial control systems, offering a practical solution that balances advanced functionality with implementation considerations.  
Future research will focus on extending this approach to multi-variable control systems, investigating the integration of reinforcement learning techniques, and developing distributed control architectures for large-scale industrial applications. Additionally, further optimization of the LSTM network structure and investigation of alternative AI/ML approaches for system identification remain important areas for continued research.  
Software, Y.-J.P.; Controller design, J.-H.C. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Wonkwnag University in 2023.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.