## 1 Introduction
To improve the robustness of the ANN based MPPT algorithm, multiple parameters of photovoltaic products are normally required to train the ANN and calculate the optimized weight coefficients. Under different environmental conditions, such as temperature, insolation, and shading patterns, solar panels can present various P-V characteristics, which means that the optimized weight coefficients of ANN could be changed to achieve the best solution for each case. In fact, weight parameters of ANNs are fixed in real-time control systems. Therefore, such ANNs may not demonstrate the best performance under all environmental conditions. For reducing the complexity of design of the MPPT algorithm, fuzzy logic control (FLC)-based MPPT algorithms have been proposed in [14,15,16,17,18,19,15,19,16,17,19,18,15,14,19,17,18,19,19,20,21,22]. Unlike ANNs, the FLC design is required to recognize patterns of P-V characteristics of photovoltaics under different operating conditions. The fuzzy logic controller used for MPPT will be designed based on those recognized patterns. In [14,15,16,17,18], the input signals of FLCs mainly consist of PV voltage, PV current, incremental change in the PV voltage, incremental change in the PV power, and the ratios of the incremental change in PV power to the change in PV voltage or current. In [19], the change in PV power, \(\Delta P\) and change in PV current, \(\Delta I\) are selected as the input signals to FLC. According to the PVs' characteristics, the derivative of PV power with respect to the PV current is zero at the MPP. Additionally, the PV power will decay to zero as the PV voltage increases to the open-circuit voltage. The output of the FLC suggested in [19] is the duty-ratio, D. The maximum step-size of this duty-ratio is about 0.8% so that no large step-up or -down change in PV current can be expected. In this case, the transition of such FLC-controlled PV system responding to the rapid weather change may be slow. In [20], the authors combined ANN with FLC for improving the MPPT performance under shading conditions. This ANN was used for estimating the optimal PV voltage and PV power, while this FLC was used as an actuator for voltage regulations. The complicated calculations proposed in [20] can indeed improve the MPPT algorithm in terms of tracking speed and convergence to the global MPP under shading conditions. However, high-performance microcontrollers are generally needed for this complex algorithm, and the additional cost of such PV systems could be expensive. In [19], this FLC can only demonstrate the tracking speed and small oscillation under the ideal cases in which the insolation and temperature conditions are relatively stable. In fact, the environmental condition variations may introduce unexpected factors to this control algorithm and further make this FLC algorithm unstable. There is also much research related to improving FLC-based MPPTalgorithms [21; 22; 23]. Even though FLC-based algorithms may differ in complexity and effectiveness, the strategies of the algorithms generally consist of three stages: estimation, execution, and observation. The significant stage among those three stages is estimation. Knowing that the P-V characteristics of solar cells can vary at different moments in a day, to be efficient, the MPPT algorithm should have capability to recognize variation of P-V characteristics of solar cells and re-evaluate the operating status of the PV system. In this paper, an improved FLC-based MPPT algorithm with a self-evaluation mechanism is proposed. The two inputs of the proposed FLC are the changes in PV voltage (\(\Delta\)V), and the derivative of power with respect to voltage, (\(\Delta\)P/\(\Delta\)V). To simplify the system structure, no irradiation and temperature signal is needed in the proposed FLC. The objective of the proposed FLC is to optimize the P and O algorithm in terms of accelerating tracking speed, suppressing steady-state power oscillations, and enhancing the robustness of the algorithm.