Adaptive Sliding Mode Control Based on Fuzzy Logic and Low Pass Filter for Two-Tank Interacting System

An adaptive sliding mode control (SMC) based on fuzzy logic and low pass filter is designed in this research. The SMC is one of the most widely accepted robust control techniques. However, the main disadvantage of the SMC is chattering phenomena, which inhibits its usage in many practical applications. Fuzzy logic control has supplanted conventional techniques in many applications. A major feature of fuzzy logic is the ability to express the amount of ambiguity in individual perception and human thinking. In this study, a fuzzy inference system is applied to approximate the function in the SMC law. A low pass filter is used to reduce chattering phenomena around the sliding surface. The stability of the control system is proved by the Lyapunov theory. The proposed controller is tested to position tracking control for two-tank interacting system. This system has been applied in process industries like petroleum refineries, chemical, paper industries, water treatment industries. Simulation results in MATLAB/Simulink show that the proposed algorithm is more effective than the sliding mode control, sliding mode control using conditional integrators and fuzzy control without steady-state error, the overshoot is 0 (%), the rising time achieves 2.187 (s) and the settling time is about 3.9133(s).

Automated summary

An adaptive sliding mode control based on fuzzy logic and low pass filter is proposed in this research to address the chattering problem in traditional sliding mode control technique. By using fuzzy inference and low pass filter, the stability of the control system is guaranteed. Simulation results on position tracking control for a two-tank interacting system demonstrate the effectiveness of the proposed algorithm compared to other methods.