## 1 Introduction  
The adoption of renewable energy production technologies has grown over the past few decades due to more demanding environmental metrics, resulting in the need to reduce CO\({}_{2}\) emissions from fossil fuels. As a result, researchers are continuously seeking new methods of maximizing the power production of solar panels [1, 2]. Using wide-bandgap semiconductors, such as silicon carbide or gallium nitride [3], and lowering computing demands [4] are two further ways to improve the effectiveness of renewable energy systems. Tracking the maximum power point (MPP) in a PV panel can be difficult due to the non-linearity of voltage vs. current in PV cells, and the variable response in their output power as a function of irradiance, temperature, solar incidence angle, and output load [5]. MPPs can be located using a variety of techniques [6, 7], and can be grouped into offline and online maximum power point tracking (MPPT) algorithms [8]. Offline techniques, which often require briefly disconnecting the PV from the load, are simple but less effective, and are not considered in this work. Online MPPT techniques provide continuous MPP monitoring under a variety of conditions, including different temperatures, solar incidence angles, and irradiance levels, and do not require the PV panels to be disconnected. Due to its simplicity and low computational requirements, perturb and observe (P&O) is widely used in industry and highly researched in academia [9; 10; 11; 12; 13]. Another well-known MPPT method is incremental conductance (INC) [14]; its search algorithm is still being researched in the literature, with authors proposing some modifications [15; 16]. Growing low-cost computing power led to the development of more complex controllers in the 1990s and 2000s, such as fuzzy logic (FL) [5; 17], neuro-fuzzy controllers [18], artificial neural networks (ANNs) [19; 20; 21; 22], and reinforcement learning [23]. Recently, several algorithms have been proposed for addressing the partial shading problem in solar energy systems. These algorithms include those based on flower pollination [24; 25], particle swarm optimization [26], and fireworks algorithm-based [27] methods that provide better tracking in cases of sudden changes in irradiation. In fact, the algorithm proposed in this work could be integrated with some of these methods or use array reconfiguration [25; 28] to improve performance under partial shading conditions, although partial shading is not specifically investigated in this study. Still, there are multiple ways in which MPPT algorithms are implemented in low-computation devices [29; 30; 31; 32; 33]. However, a comparative study of the hardware resources needed for implementing them could not be found in the literature. In this study, a new MPPT method is proposed that combines the incremental conductance concept with an integral compensator (IC-INC). This novel approach overcomes the constant increment or decrement value associated with traditional MPPT algorithms, such as P&O and INC, which can limit their effectiveness. By using linear feedback theory to obtain the MPP, the proposed IC-INC MPPT method eliminates this problem and offers simple implementation in a digital context that does not require many hardware resources. The use of an integral controller to regulate incremental conductance represents a significant contribution to the literature on MPPT. To evaluate the effectiveness of the proposed method, a comparison is made with well-known MPPT algorithms found in the literature, including P&O-, INC-, FL-, and ANN-based approaches using irradiance and temperature. The proposed IC-INC method is also compared with an ANN trained on a dataset generated from IC-INC. The evaluation is carried out under time-varying irradiance conditions, and the compared methods are analyzed in terms of power extracted from the PV panel, response time, oscillations around the MPP, and hardware resources requirements.