## 9 Conclusions  
This work demonstrates the effectiveness of intelligent control strategies, particularly Artificial Neural Networks (ANNs) and Deep Q-Networks (DQNs), in enhancing the performance of photovoltaic systems through MPPT optimization. The implemented MATLAB/Simulink models, which simulated both uniform and partial shading scenarios, provide a robust framework for comparative analysis. The results show that AI-based methods not only achieved superior tracking efficiency and faster convergence times but also exhibited greater resilience to dynamic changes and adaptability to non-linear environmental conditions. Among all the tested algorithms, the DQN agent consistently outperformed the others in PSC scenarios, confirming its ability to identify the GMPP more effectively. These findings underscore the potential of integrating learning-based controllers into PV systems to improve energy harvesting in real-world applications. A natural progression of this research involves the integration of a bidirectional DC/DC converter, specifically a buck-boost topology, which would enable both the charging and discharging of an energy storage system. This architecture would facilitate the transition from a passive PV system to a hybrid energy management solution capable of supplying loads autonomously during periods of low solar generation. Additionally, future work could focus on the experimental validation of the proposed MPPT algorithms under real-world operating conditions. This would involve developing a physical prototype that integrates photovoltaic panels, bidirectional converters, embedded controllers (e.g., microcontrollers), and appropriate sensors for current, voltage, irradiance, and temperature measurement. Implementing the control logic directly in embedded hardware would allow for the assessment of real-time performance, computational constraints, and resilience to disturbances, such as measurement noise or sudden environmental changes. Another promising research direction includes the development of hybrid renewable energy systems, combining solar and wind energy sources under a unified control framework. The MPPT algorithms could be extended or adapted to coordinate multiple energy sources with complementary profiles, enhancing system reliability and reducing energy intermittency. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{8}{c}{**Evaluation Criteria**} \\ \cline{2-10}  & \multirow{2}{*}{**Compl.**} & \multirow{2}{*}{**Cost**} & \multirow{2}{*}{**Param. Ind.**} & \multirow{2}{*}{**Training**} &
\begin{tabular}{c} **Conv.** \\ **Speed** \\ \end{tabular} & \multirow{2}{*}{**Sensors**} & \multirow{2}{*}{**Tuning**} & \multirow{2}{*}{**Stability**} & \multirow{2}{*}{**PSC**} \\ \hline P\&O & Low & Medium & Yes & No & Fast & V,I & No & Low & Weak \\ InC & Medium & Medium & Yes & No & Fast & V,I & No & Low & Weak \\ FLC & High & Medium & Yes & No & Fast & V,I & No & High & Good \\ InC + P\&O & Medium & Medium & Yes & No & Fast & V,I & No & Low & Weak \\ InC + GA & High & High & Yes & No & Slow & V,I & No & Medium & V.Good \\ P\&O + GA & High & High & Yes & No & Slow & V,I & No & Medium & V.Good \\ ANN & High & High & No & Yes & V,Fast & V,I,G,T & Yes & High & Exc. \\ DQN (RL) & High & High & No & Yes & Fast & V,I,G,T & Yes & High & Exc. \\ \hline \hline \end{tabular}  
* Notes: Abbrev: Compl. = Complexity, Param. Ind. = Parameter Independence, Conv. Speed = Convergence Speed, Sensors; Stability: Low (oscillations), Medium (small variations), High (stable); PSC: Weak (oscillations), Mod. (moderate), Good, V.Good, Exc. (Excellent); Sensors: V = Voltage, I = Current, G = Irradiance, T = Temperature.  
\end{table} 
Table 22: Comparison of MPPT algorithms. **Author Contributions:** Conceptualization, J.T.S. and R.S.B. ; methodology, J.T.S. and R.S.B. ; software, J.T.S. ; validation, J.T.S. and R.S.B. ; formal analysis, J.T.S. ; investigation, J.T.S. ; writing--original draft preparation, J.T.S. ; writing--review and editing, J.T.S. and R.S.B.