Similar articles

Abstract: 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.

This is an interesting work of the application of AFISMC to a practical system. It contains both theoretical analysis and experimental validation. The paper was generally well written and the derivation seems correct.

双轮铣槽机铣削系统自适应模糊积分滑模转速控制

[1]Ahn, K.K., Chau, N.H.T., Truong, D.Q., 2007. Robust force control of a hybrid actuator using quantitative feedback theory. J. Mech. Sci. Technol., 21(12):2048-2058.

[2]Busquets, E., Ivantysynova, M., 2015. Discontinuous projection-based adaptive robust control for displacement-controlled actuators. J. Dyn. Syst. Meas. Contr., 137(8):081007.

[3]Cerman, O., 2013. Fuzzy model reference control with adaptation mechanism. Expert Syst. Appl., 40(13):5181-5187.

[4]Chen, C.Y., Liu, L.Q., Cheng, C.C., et al., 2008. Fuzzy controller design for synchronous motion in a dual-cylinder electro-hydraulic system. Contr. Eng. Pract., 16(6):658-673.

[5]Chiang, M.H., Lee, L.W., Tsai, J.J., 2004. The concurrent implementation of high velocity control performance and high energy efficiency for hydraulic injection moulding machines. Int. J. Adv. Manuf. Technol., 23(3):256-262.

[6]Chiang, M.H., Yeh, Y.P., Yang, F.L., et al., 2005. Integrated control of clamping force and energy-saving in hydraulic injection moulding machines using decoupling fuzzy sliding-mode control. Int. J. Adv. Manuf. Technol., 27(1):53-62.

[7]Daher, N., Ivantysynova, M., 2013. System synthesis and controller design of a novel pump controlled steer-by-wire system employing modern control techniques. Proc. ASME/BATH Symp. on Fluid Power and Motion Control, p.1-10.

[8]Daher, N., Ivantysynova, M., 2014. An indirect adaptive velocity controller for a novel steer-by-wire system. J. Dyn. Syst. Meas. Contr., 136(5):051012.

[9]Guo, K., Wei, J.H., Fang, J.H., et al., 2015. Position tracking control of electro-hydraulic single-rod actuator based on an extended disturbance observer. Mechatronics, 27:47-56.

[10]Kalyoncu, M., Haydim, M., 2009. Mathematical modelling and fuzzy logic based position control of an electrohydraulic servosystem with internal leakage. Mechatronics, 19(6):847-858.

[11]Lin, J., Huang, Z.Z., 2007. A hierarchical fuzzy approach to supervisory control of robot manipulators with oscillatory bases. Mechatronics, 17(10):589-600.

[12]Lin, Y., Shi, Y., Burton, R., 2013. Modeling and robust discrete-time sliding-mode control design for a fluid power electrohydraulic actuator (EHA) system. IEEE/ASME Trans. Mech., 18(1):1-10.

[13]Merritt, H.E., 1967. Hydraulic Control Systems. John Wiley & Sons, New York, USA.

[14]Minav, T.A., Laurila, L.I.E., Pyrhönen, J.J., 2013. Analysis of electro-hydraulic lifting system’s energy efficiency with direct electric drive pump control. Autom. Constr., 30:144-150.

[15]Sha, D.H., Bajic, V.B., Yang, H.Y., 2002. New model and sliding mode control of hydraulic elevator velocity tracking system. Simul. Practice Theory, 9(6-8):365-385.

[16]Shi, Y., Huang, J., Yu, B., 2013. Robust tracking control of networked control systems: application to a networked DC motor. IEEE Trans. Ind. Electron., 60(12):5864-5874.

[17]Truong, D.Q., Ahn, K.K., 2009. Force control for hydraulic load simulator using self-tuning grey predictor—fuzzy PID. Mechatronics, 19(2):233-246.

[18]Truong, D.Q., Ahn, K.K., 2011. Force control for press machines using an online smart tuning fuzzy PID based on a robust extended Kalman filter. Expert Syst. Appl., 38(5):5879-5894.

[19]Wang, D.Y., Lin, X., Zhang, Y., 2011. Fuzzy logic control for a parallel hybrid hydraulic excavator using genetic algorithm. Autom. Constr., 20(5):581-587.

[20]Wang, L.K., Book, W.J., Huggins, J.D., 2012. Application of singular perturbation theory to hydraulic pump controlled systems. IEEE/ASME Trans. Mech., 17(2):251-259.

[21]Wang, X.J., Wang, S.P., Zhao, P., 2012. Adaptive fuzzy torque control of passive torque servo systems based on small gain theorem and input-to-state stability. Chin. J. Aeronaut., 25(6):906-916.

[22]Wei, J.H., Guo, K., Fang, J.H., et al., 2015. Nonlinear supply pressure control for a variable displacement axial piston pump. Proc. Inst. Mech. Eng. Part I: J. Syst. Contr. Eng., 229(7):614-624.

[23]Wei, L., Fang, F., Shi, Y., 2014. Adaptive backstepping-based composite nonlinear feedback water level control for the nuclear U-tube steam generator. IEEE Trans. Contr. Syst. Technol., 22(1):369-377.

[24]Wu, H.W., Lee, C.B., 1995. Self-tuning adaptive speed control of a pump/inverter-controlled hydraulic motor system. Proc. Inst. Mech. Eng. Part I: J. Syst. Contr. Eng., 209(29):101-114.

[25]Zhang, H., Shi, Y., Mu, B.X., 2013. Optimal H_∞-based linear-quadratic regulator tracking control for discrete-time Takagi-Sugeno fuzzy systems with preview actions. J. Dyn. Syst. Meas. Contr., 135(4):044501.