An improved sliding mode control combined with backstepping techniques and artificial neural networks for a coupled-tank system
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https://doi.org/10.54939/1859-1043.j.mst.112.2026.20-28Keywords:
Coupled-tank system; Sliding mode control; Backstepping; Radial basis function neural networks; MATLAB/Simulink.Abstract
This study proposes a solution to design a liquid level tracking controller for a coupled-tank system (C-TS) using a sliding mode control (SMC) method based on proportional integral (PI) sliding surface (SS) combined with backstepping techniques and radial basis function neural networks (RBFNNs). The SMC controller based on proportional integral sliding surface (also called PISMC) provides more parameters with which to tune the SMC controller. The backstepping approach ensures the global asymptotic stability of strict feedback systems. The improved backstepping sliding mode control is employed to enhance the performance of conventional sliding mode control and act as a robust control strategy. The RBFNNs are utilized to approximate the unknown nonlinear functions in the improved backstepping sliding mode control. System stability is proven through Lyapunov theory. Simulation results in MATLAB/Simulink demonstrate the effectiveness, appropriateness, and robustness of the proposed control method in comparison with the fuzzy controller, adaptive control using RBF neural network, sliding mode control (SMC) based on disturbance observer and Quasi mode and sliding mode control using conditional integrators with the rising time achieves 0.1295 (s), the settling time is 0.2233 (s), the percent overshoot is 0 (%), the steady state error converges to zero.
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