## 2 Complete System Formation
### A Hybrid Proposed FLC-ANN Tuned FPA MPPT  
In this proposed scheme, the hybrid ANFIS-FPA MPPT algorithm is realized for maximizing PV outturn and accurate motion control with PV-pump interface. The FLC data is trained by ANN which is finally optimized by FPA method, leading to minimum RMSE of FLC and ANN. It comprises the dominance of both FLC and ANN. The threshold and weight of NN models are optimized by FPA algorithm to produce minimum RMSE. Figure 4 depicts the complete structure of hybrid learning in which learning data has been achieved from FLC architecture. The FLC architecture comprises fuzzification, Inference Rule base and defuzzification as elemental constituents. Real variables are converted to linguistic parameters using fuzzification. The requisite output is introduced by the Mamdani fuzzy inference rule deployed by max-min composition. With the help of centroid method, the defuzzification process converts the linguistic parameters to real values. Parameters used in FLC and ANN are presented using Table 2. Employed membership values are illustrated in Figure 5. The FPA method of MPPT is predicted by reproduction of flower of transferring pollen. This convection is possible through biotic/cross and abiotic/self-pollination. In cross-pollination the pollens are translated between two unlike flowers. On the other hand, abiotic pollination takes place between distant species. It is noted that in flower pollination 90% possibility of cross-pollination and only 10% possibility of self-pollination happen, which is limited in the probability range Re[1,0]. Table 3 describes ANFIS-FPA parameters used for practical validation of BLDC-driven PV pumping. The complete process is based on the following 4 rules [22]. The min-max composition ([PERSON]'s rule) is employed to calculate the fuzzy error (\(E\)) and change in error (\(dE/CE\)) input as [23]:  
\[E(r)=\frac{dP_{PV}(r)}{dV_{PV}(r)} \tag{6}\]  
\[dE=E(r)-E(r-1) \tag{7}\]  
\[\mu_{P\to Q}(xy)=min\big{[}\mu_{P}(x),\mu_{Q}(y)\big{]},\hskip 42.679134 pt \forall P\in X,\ \forall Q\in Y \tag{8}\]  
where,  
\(\mu_{P}(x)\) = Membership function of \(P\) fuzzy set in \(X\) Universe of discourse  
\(\mu_{Q}(y)\) = Membership function of \(Q\) fuzzy set in \(Y\) Universe of discourse  
\(X,Y=x\), \(y\) variables defined in Universe \(X\) and \(Y\), respectively
Output \(D\) is calculated as:  
\[\hat{D}=\frac{D\in\mathbb{S}\int\mu_{D}(D)dD}{D\in\mathbb{S}\int\mu_{D}(D)} \tag{9}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**S.N** & **Parameters** & **Value** \\ \hline
1 & Total fuzzy rule base fired & 25 \\
2 & Total number of Epoch & 740 \\
3 & Types of membership function & Gaussian type \\
4 & Total layer (neural network) & 5 \\
5 & Total neural network training data sets & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters used in FLC and ANN. Figure 4: Complete structure of hybrid ANFIS-FPA. Figure 5: Employed membership values. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S.N** & **Parameters** & **Values** \\ \hline
1. & Switched Probability (\(P_{f}\)) & 0.7 \\
2. & Scaling Factor & 1.25 \\
3. & No of Epoch & 740 \\
4. & RMSE (Obtained) & \(106\times 10^{-6}\) \\
5. & Total Rule Based Fired & 25 \\
6. & ANFIS Obtained (Training Error) & \(0.6255\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ABC-FPA Parameters.