Abstract—This paper proposes a New Adaptive Neural Fuzzy Sliding Mode Controller (NANFSMC) for regulating a Coupled Tank System (CTS), with unknown nonlinear dynamics in experimental environments. The CTS exhibits strong nonlinearities and uncertainties arising from sensor noise, parameter variations, variations in output valve characteristics, and significant time delays. The proposed control architecture integrates two synergistic components. The first component is an adaptive control system that utilizes a Radial Basis Function Neural Network (RBFNN) to approximate the adaptive control law, featuring an adaptive updating mechanism to compensate for RBFNN approximation errors. The second component is a Sliding Mode Control (SMC) system, whose parameters are updated in real-time via a fuzzy inference mechanism to enhance robustness. Both control laws are derived within the framework of Lyapunov stability theory, ensuring closed-loop stability under all operating conditions. The proposed controller possesses a simple structure, resulting in low computational load and requiring only a few tuning parameters. Although the RBFNN weights are initialized to 0, the integration with the adaptive fuzzy mechanism allows fast convergence and rapid stabilization. Furthermore, this study presents the first experimental validation of a Takagi-Sugeno (TS)-fuzzy–based adaptive tuning of the SMC robustness gain on a real CTS under external disturbances. The proposed method achieves improvements of up to 22.9% and 14.2% in the Integral of Absolute Error (IAE), Mean Absolute Error (MAE), and Integral of Time-weighted Absolute Error (ITAE) indices compared to the Adaptive Neural SMC (ANSMC) and Proportional Integral Derivative (PID) controllers, respectively.

Keywords—Proportional Integral Derivative (PID), approximation error, real-time validation, external disturbance, robustness, computational load

Cite: Nguyen Anh Tuan and Ho Pham Huy Anh, "A New Adaptive Neural Fuzzy Sliding Control Method for Dynamic Nonlinear Plants," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 1, pp. 80-92, 2026. doi: 10.18178/ijmerr.15.1.80-92

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