Nowadays, renewable energy sources (RES) such as photovoltaic (PV) solar systems, wind turbines, and others are integrated into conventional power systems to avoid the high cost of constructing new or expanded facilities. The final stage of the integration of PV systems consists of DC-AC inverters. Three-phase inverters are widely used in grid-connected renewable energy systems. Grid-connected PV inverter systems are very unpredictable and nonlinear systems since PV is intermittent and inverters employ Pulse Width Modulation (PWM) technology. When there is no quick-acting inverter control, these nonlinearities and uncertainties cause problems with power quality, output harmonics, voltage regulation, losses, and system implementation. This research work presents a new intelligent control methodology for PV grid-connected inverters using type-1 and type-2 adaptive fuzzy control (AFC) techniques. The implementation of the proposed controller does not need prior knowledge of the system's mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system (GCIS) are exploited to design the controller. The proposed controller is capable of achieving the control objectives in the presence of both parametric and modelling uncertainties. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed type-1 controller is compared with a conventional proportional-integral (PI) controller and a Takagi– Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers. And the proposed type-2 fuzzy controller is compared to a type-1 fuzzy controller in terms of some performance measures. The comparison results demonstrate that the proposed type-2 fuzzy controller has better tracking performance than the type-1 fuzzy controller in terms of the settling time, the maximum overshoot, the integral absolute error (IAE), and the integral time of absolute error (ITAE). Finally, a lab-scale testbed is used for the first time to evaluate the real-time performance of the proposed adaptive fuzzy controller algorithms for GCISs. In the Hybrid Station Lab at the Sustainable Energy Research Center of Sultan Qaboos University, experimental validation was carried out under various operating conditions. The results of the experiments show that the proposed adaptive fuzzy controller has outstanding tracking capability, quick response times, and fast convergence of the adaptive parameters.