## 2 Methodology
### Proposed Tracking Strategy
Considering the defined periods, the energy consumption on both axes is 0.26 Wh for the DMA and 0.47 Wh for the CA, and the total energy consumption is 1.27 Wh for the DMA and 1.48 Wh for the CA. Hence, the proposed algorithm demonstrates the reduction of energy consumption, representing a 14.18% reduction. The electrical power generated by the PV modules for DMA and CA is shown in Figure 9. The PV technology produces, during the tests, 19.87 Wh and 19.91 Wh with the STS\({}_{1}\) and STS\({}_{2}\), respectively. Therefore, the energy productivity for both strategies is practically identical, representing only 0.21% of difference between the strategies. Thus, the DMA simultaneously achieves a high energy conversion efficiency and reduces the number of tracking movements. For both strategies, two events of drastic reduction in the transformation of solar energy are presented in the measurements, and the reason is the cloudiness. Figure 8: Tracking error measurements of the DMA and the CA for the elevation axis. Figure 9: Electrical power generation by the PV modules with the DMA and CA strategies. The operation states for the decision-making algorithm associated with the irradiance are shown in Figure 10, where the state 0 corresponds to the \(STS_{1}\) turning to the home position and the state 1 is the movement to the next point of the trajectory, depicting the 8.33% of the tests time. In state 2, the system held position while the \(P_{pv}\) condition was accomplished, representing 90.24% of the experimentation duration. Finally, the system maintains its position while the \(I_{tot}\) condition is true in state 3, with 1.44% of the remaining time. In addition, five alarms were registered during the tests due to the partly cloudy condition. Consequently, the productivity analysis for both strategies is performed with the experimental results obtained and shown in the bar graph in Figure 11, where \(E_{c,a}\) and \(E_{c,e}\) are the required energy for the azimuthal and elevation axis, respectively. \(E_{c,m}\) is the energy consumption of the monitoring device and \(E_{PV}\) is the energy produced by the solar modules. In addition, the energy balance of the strategies is shown by calculating \(E_{tot}\) with Equation (1). Table 2 shows the summarized results of the energy productivity. Based on the experimental results, the CA strategy segments the trajectory into 15 steps and the DMA strategy into seven steps, as shown in Figure 8, which implies a 53.33% reduction in steps. Likewise, the total operation time for each axis of both strategies was determined (see Figure 12), obtaining a total time of 232 s for CA and 91 s for DMA, which implies a 60.77% reduction in operation time with the proposed strategy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \(\varepsilon_{\beta}\) [\({}^{\circ}\)] & \(\varepsilon_{\gamma}\) [\({}^{\circ}\)] & \(\varepsilon_{m}\) [\({}^{\circ}\)] & \(E_{g}\) [Wh] & \(E_{c}\) [Wh] & \(E_{tot}\) [Wh] \\ \hline DMA & 1.43 & 4.39 & 4.61 & 19.87 & 1.27 & 18.60 \\ \hline CA & 0.27 & 1.00 & 1.03 & 19.91 & 1.49 & 18.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the experimental results of tracking error and energy analysis. Figure 10: States of operation by the DMA and the measured irradiance variation. Figure 11: Bar graph of the energy balance of monitoring strategies. Figure 12: Operation times for the DMA and CA strategies. On the other hand, for the estimation of the service life, the following considerations are taken into account:

* For the calculation, the payload was evaluated as a constant value, only the load of the PV module; however, an increase in the applied load due to the wind force on the solar collection surface exists, affecting the study, and hence, it must be considered in future calculations.