<p>This paper presents the design of a novel adaptive fuzzy neural sliding mode controller (NAFNSMC) for controlling the quadrupled tank system (QTS), aiming to address the adaptive control problem for the nonlinear system with unknown model dynamics and to improve response speed and quality under significant external disturbances. The QTS represents a highly nonlinear multi-input multi-output (MIMO) system with uncertainties such as sensor noise, parameter variations, changing outlet valve positions, and powerful cross-coupling interactions between tanks, significantly increasing the system's nonlinearity. The proposed controller consists of two main components. The first is the adaptive control component, which approximates the adaptive control law using Radial Basis Function neural networks (RBFNN). This component acts as the primary controller, compensating for the RBFNN's approximation errors, sensor noise, nonlinearities induced by cross-tank interactions, and external disturbances. The second concerns the sliding mode control component, which ensures system stability. The parameters of this controller are adaptively updated via the adaptive fuzzy controllers. The adaptive law and the sliding mode control law are designed based on Lyapunov stability theory, thus ensuring overall system stability. The effectiveness of the proposed control algorithm is verified through experimental evaluations and compared with a conventional Proportional-Integral-Derivative (PID) controller. Experimental results confirm that the proposed algorithm achieves fast and stable control performance with high adaptability to uncertainties, including sensor noise, significant external disturbances, and varying reference inputs.</p>