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Adaptive fuzzy integral sliding mode velocity control for the cutting system of a trench cutter
Qi-yan TIAN,Jian-hua WEI,Jin-hui FANG,Kai GUO
Front. Inform. Technol. Electron. Eng.    2016, 17 (1): 55-66.   DOI: 10.1631/FITEE.15a0160
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This paper presents a velocity controller for the cutting system of a trench cutter (TC). The cutting velocity of a cutting system is affected by the unknown load characteristics of rock and soil. In addition, geological conditions vary with time. Due to the complex load characteristics of rock and soil, the cutting load torque of a cutter is related to the geological conditions and the feeding velocity of the cutter. Moreover, a cutter’s dynamic model is subjected to uncertainties with unknown effects on its function. In this study, to deal with the particular characteristics of a cutting system, a novel adaptive fuzzy integral sliding mode control (AFISMC) is designed for controlling cutting velocity. The model combines the robust characteristics of an integral sliding mode controller with the adaptive adjusting characteristics of an adaptive fuzzy controller. The AFISMC cutting velocity controller is synthesized using the backstepping technique. The stability of the whole system including the fuzzy inference system, integral sliding mode controller, and the cutting system is proven using the Lyapunov theory. Experiments have been conducted on a TC test bench with the AFISMC under different operating conditions. The experimental results demonstrate that the proposed AFISMC cutting velocity controller gives a superior and robust velocity tracking performance.


Parameter Value
λ 20
k 11 500
k21 100
Φ 0.5
k11 20
k12 10-6
k21 10-6


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Table 2 AFISMC control parameters
Extracts from the Article
The third algorithm was the proposed AFISMC. The unknown bound of the disturbance increases the complexity of the calculation of the feedback gain of the reaching controller to ensure theoretical rigor. An alternative pragmatic approach is simply to choose a large enough feedback gain without worrying about specific prerequisites. This approach increases the tuning efficiency. The fuzzy system parameters were selected according to engineering experience and the control gains were tuned by trial and error based on the desired dynamic response. The boundary layer thickness (Φ) was selected on the basis of a compromise between the chattering phenomenon and the tracking accuracy. The proposed AFISMC parameters are shown in Table 2.
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