I. INTRODUCTION
A photovoltaic (PV) array under uniform irradiance exhibits a current-voltage characteristic with a unique maximum power point (MPP) where the array produces maximum output power, which changes as a consequence of the variation of the irradiance level and of the panels' temperature [1]. The issue of maximum power point tracking (MPPT) has been addressed in different ways in the literature [2]–[10]: fuzzy logic, neural networks, pilot cells and DSP based implementations have been proposed. But, especially for low-cost implementations, the Perturb and Observe (P&O) and INcremental Conductance (INC) [2] techniques are widely used. In a typical P&O MPPT algorithm, the operating voltage of the PV array is perturbed by changing the duty-cycle in a given direction (increase or decrease) and the power drawn from the PV array is probed: if it increases, then the operating voltage is further perturbed in the same direction, whereas, if it decreases, then the direction of operating voltage perturbation is reversed. A drawback of P&O is that the operating point oscillates around the MPP, even during sunny days when the irradiance is slowly varying, giving rise to the waste of some amount of available energy. Several improvements of the P&O algorithm have been proposed in order to reduce the amplitude of oscillations around the MPP in steady state, at the price of slowing down the speed of response of the algorithm to changing atmospheric conditions and lowering the algorithm efficiency during cloudy days. The INC algorithm seeks to overcome such limitations. However, as discussed in [12], because of noise and measurement and quantization errors, also the INC operating voltage oscillates around the MPP. Both methods can be confused during those time intervals characterized by changing atmospheric conditions, since the operating point can move away from the MPP instead of close to it [2]. In [12] it is shown that the P&O method, when properly optimized, leads to an efficiency which is equal to that obtainable by the INC method; however, no guidelines or general rules are provided therein allowing the identification of the optimal values of P&O parameters which are instead chosen through trial and error tests. This paper shows that the efficiency of P&O MPPT control technique can be improved by optimizing its sampling rate according to the converter's dynamics. As an example, a boost MPPT converter (fig. 1) has been studied. A boost MPPT converter schematic.