###### Abstract  
Conventional control methods, which follow the maximum power point (MPP), suffer from being slow or inaccurate during sudden changes in irradiance and temperature. These problems can be solved using artificial intelligence algorithms. The current study proposes a new multi-degree-of-freedom (MDOF) fuzzy logic controller (FLC) for maximizing the overall output performance of a photovoltaic system. The MDOF-FLC was compared to the simplified universal intelligent PID controller (SUI-PID) using the MDOF concept and the normal FLC. Simulation and experimental results show that the proposed MDOF-FLC controller has a 37.8% and 58.1% faster response with a better rise time compared to the SUIPID controller and the normal FLC, respectively. At the same time, the error, measured by the integral time absolute error (ITAE), was 29.4% and 62.5% lower, respectively. photovoltaic system; MPPT; MDOF concept; MDOF-FLC; SUI-PID; DC pump +
Footnote †: journal: Applied system  
a variable step size to determine the best value of the duty cycle, resulting in enhanced response time and stability under a wide range of operational circumstances [4; 5; 6; 7; 8; 9; 10]. [PERSON] et al. [5] pioneered using an adaptive neuro-fuzzy inference system (ANFIS) reference model. Additionally, they built a method of constant voltage and incremental conductance (IC) on a field-programmable gate array board to test and evaluate their performance. The results indicate that the ANFIS reference model is more effective and has a better dynamic response when compared to the IC and constant voltage techniques. [PERSON] et al. [6] improved the MPPT's accuracy by proposing a new overall distribution (OD) algorithm that was combined with a particle swarm optimization (PSO) algorithm. They demonstrated that using the OD-PSO MPPT algorithm, the optimal particle positions for the PSO algorithm could be determined. Additionally, the OD-PSO algorithm outperformed conventional methods based on Firefly and PSO. [PERSON] et al. [7] demonstrated a technique for three-phase voltage source inverter current control using fuzzy space vector pulse width modulation (FSVPWM). They achieved excellent tracking efficiency and optimal MPP in adverse operating conditions by utilizing a hybrid fuzzy particle swarm optimization technique. The results reveal more excellent performance when load and parameter fluctuations are nonlinear. [PERSON] et al. [11] proposed Hybrid Bat MPPT algorithms using established techniques like IC, beta, and perturbation and observation (P&O). They obtained more accurate results when solar irradiance and load varied slightly. [PERSON] et al. [12] presented an effective tracking of the MPP using a radial basis function neural network (RBFNN) for a water pumping system. The results indicate that the RBFNN MPPT algorithm more efficiently tracks MPP than the P&O and IC methods. [PERSON] et al. [13] proposed a new control algorithm (SUI-PID) for determining the MPP of a solar pump system working under various operational situations. The SUI-PID enhances the dynamic response compared to the FLC by 32.7%. Additionally, an increase in the water flow rate was achieved. The present work proposes a new MDOF-FLC for the MPPT to improve the performance of the photovoltaic system. The proposed algorithm consists of two controllers: the first deals with significant errors (the fast controller), while the other deals with minor errors in the fine-tuning region (the slow controller). An error spike is caused by abrupt changes in temperature, irradiance, or both. The fast controller is preferred in transient conditions (regions where the error is significant), whereas the slow controller works in a steady state (areas where the error is minor). As a result, depending on the actual operating conditions, the MDOF-FLC combines the advantages of both rapid and slow controllers. The controller's response is expected to be faster, with a better rise time and minimum overshoot.