## 3 Results and Discussion
### Performance Analysis  
The performance of the proposed hybrid control system was evaluated comprehensively against conventional methods across multiple metrics, including control performance indices, temporal response characteristics, and computational resource utilization. Table 4 presents a detailed comparison between the conventional and hybrid approaches.  
The control performance indices show significant improvements with the hybrid method. The Integral Absolute Error (IAE) was reduced from 317.4142 to 275.0000, representing a 13.4% improvement. Similarly, the Integral Square Error (ISE) showed a 12.3% reduction from 146.1803 to 128.0000, while the Integral Time Absolute Error (ITAE) demonstrated a 15.0% improvement, decreasing from 3.1741 to 2.7000. These improvements in error metrics indicate superior tracking performance and disturbance rejection capabilities in the hybrid system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Conventional** & **Hybrid Method** & **Improvement** \\ \hline IAE & 317.4142 & 275 & 13.4\% \\ ISE & 146.1803 & 128 & 12.3\% \\ ITAE & 3.1741 & 2.7 & 15\% \\ Rise Time (s) & 0.85 & 0.62 & 27.1\% \\ Settling Time (s) & 2.45 & 1.85 & 24.5\% \\ Overshoot (\%) & 15.3 & 8.7 & 43.1\% \\ CPU Usage (\%) & 100 & 65 & 35\% \\ Memory Usage (MB) & 256 & 102 & 60.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comprehensive performance comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{LSTM Network} & Input Neurons & 64 \\  & Hidden Layers & 2 \\  & Neurons per Layer & 32 \\  & Output Neurons & 3 \\  & Training Epochs & 100 \\  & Batch Size & 32 \\ \hline \multirow{6}{*}{Genetic Algorithm} & Population Size & 50 \\  & Generations & 100 \\ \cline{1-1}  & Mutation Rate & 0.1 \\ \cline{1-1}  & Crossover Rate & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hybrid control system configuration parameters.  
Figure 3 demonstrates the time response using the proposed method, showing significantly improved temporal response characteristics. The rise time was reduced by 27.1%, from 0.85 s to 0.62 s, indicating faster system response. The settling time showed a 24.5% improvement, decreasing from 2.45 s to 1.85 s, demonstrating enhanced stability characteristics. Most notably, the system overshoot was reduced by 43.1%, from 15.3% to 8.7%, highlighting the superior damping characteristics of the hybrid approach.  
In terms of computational efficiency, the hybrid method demonstrated substantial improvements in resource utilization. CPU usage was reduced by 35.0%, from 100% to 65%, indicating more efficient processing. Memory usage showed the most significant improvement, with a 60.2% reduction from 256 MB to 102 MB, making the system more suitable for implementation in resource-constrained environments.  
The comprehensive performance improvements can be attributed to several factors:  
1. The LSTM network's ability to capture complex system dynamics and adapt to changes in real-time.
2. The Genetic Algorithm's efficient optimization of control parameters.
3. The synergistic integration of traditional control methods with AI/ML techniques.
4. The optimized implementation of the hybrid architecture.  
These results demonstrate that the proposed hybrid approach not only improves control performance but also achieves significant reductions in computational resource requirements, making it a practical solution for industrial applications requiring both high performance and efficiency.