## 1 Introduction  
Solar power is one of the most cleanest and abundant energy source in the world. It is virtually inexhaustible and is likely to be our main source of power in the future [1,2]. Solar power has already been widely applied for industrial, commercial, residential and military purposes. Solar power generated by solar or photovoltaic (PV) cells depends on the environmental conditions such as irradiations, sunlight incident angle, cell temperature, and load conditions. Power converters would be typically inserted between the PV cells and the load to control the power flow from the PV cells to the load. Many PV systems also employ some form of maximum power point tracking (MPPT) to maximize power output, with the MPPT function achieved through continuously adjusting the duty ratio command of the power switch in the power converter. MPPT algorithms maximize power output by gradually increasing or decreasing the duty ratio of the power converter according to the PV cell output power _versus_ the voltage curve or the current _versus_ voltage curve. Commonly used MPPT techniques include the perturbing and observing method [3] and the incremental conductance method [4], both methods using fixed step size for the increment of the duty ratio command. If the step size is too small, the tracking process would be slowed. If the step size is too large, then the system may fluctuate about the maximum power point (MPP). In order to automatically adjust step sizes, variable step sizing algorithms based on adaptive and artificial intelligent techniques such as fuzzy logic and adaptive neuro-fuzzy system were assessed [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. Fuzzy logic controllers are characterized by their ability to imitate human thinking. Unlike traditional controllers, fuzzy controllers are able to use empirical methods or professional knowledge to design variable step size increments of duty ratio command for the power converter even without having an understanding of the mathematical model of the plant. Design considerations and effectiveness of the fuzzy MPPT algorithm depend on the input and output variables selected for the system. The output variable of the fuzzy MPPT algorithm would usually be the duty ratio command of the power switch for the power converter. However, there would be a number of different MPPT algorithm input variables to choose from based on the characteristics of the PV cells. The most commonly used input variables for the MPPT algorithms would be the slope of the power-_versus_-voltage (P-V) curve of the PV cell characteristics curves and changes of this slope [10; 11; 12; 17]. In a previous study [9], P-V slope and variation of power \(\Delta P_{PV}\) were selected as the input variables, while another study [13,18] selected variations of power and voltage (\(\Delta P_{PV}\) and \(\Delta V_{PV}\) ) as the input variables instead. Other studies also used variations of power and current (\(\Delta P_{PV}\) and \(\Delta I_{PV}\) ) [14]. Previous investigations adopting the incremental conductance method used conductance and increment of conductance as the fuzzy input variables for MPP evaluation [15]. Another reported fuzzy MPPT algorithm used the sum of the arctangent of the conductance and arctangent of the conductance increment as the input variable [16]. Despite the numerous combinations for input variables selected, design considerations for the determination of fuzzy rules were usually not discussed in detail in the literature. In this paper, we have focused upon the design of solar power MPPT algorithms. Special emphases were provided on the considerations for determining associated fuzzy rules for different fuzzy input variables.