Abstract
About the control of articulated robot, researchers put out an adaptive fuzzy sliding mode control algorithm by analyzing the classical sliding mode algorithm. There is an adaptive single input and output fuzzy to calculate the control gain. Meanwhile, researchers designed a adaptive law which is based on Lyapunov theory, and simulated the adaptive fuzzy sliding mode control in Simulink. Simulink results show that when the chattering exiting becomes weaker, the function of system is stronger. Since the adaptive algorithm joined, fuzzy sliding mode control can be different with the change of system’s state and adjusted automatically. Steady-state convergence is constant, adaptive fuzzy sliding mode control algorithm still with a good robustness under the condition that articulated robot’s parameters uncertain and external interference.
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Ning, X.T., Chen, J., Shi, Q., Pan, Y.T.: Simulation of trajectory planning for robot workspace. Comput. Simul. 33(2), 367–372 (2016)
Yang, L.J., Shen, L.Y., Ding, H.: The trajectory planning simulation of 4-DOF upper limb rehabilitation robot. Comput. Simul. 33(8), 332–337 (2016)
Gao, C.: Trajectory planning of welding robot based on terminal priority planning. Sens. Transducers 169(4), 111–116 (2014)
Li, H.K., Shi, A.J., Dai, Z.D.: A trajectory planning method for sprawling robot inspired by a trotting animal. J. Mech. Sci. Technol. 31(1), 327–334 (2017)
Li, Z.X., Shi, S.Z., Wang, H., Zhao, J.Q.: Simulation of optimal control model for robot motion. Comput. Simul. 33(5), 280–284 (2016)
Liu, J.K., Sun, F.H.: Research and development on theory and algorithms of sliding mode control. Control Theor. Appl. 24(3), 407–418 (2007)
Hu, S.B., Lu, M.: Adaptive double fuzzy sliding mode control for three-links spatial robot. J. Tongji Univ. (Nat. Sci.) 40(4), 622–628 (2012)
Wu, J.F., Li, Y.: The reseach on the application of two-wheeled self-balancing robot based on the method of variable structure control. J. Harbin Univ. Sci. Technol. 18(2), 95–100 (2013)
Rossomando, F.G., Soria, C.M.: Adaptive neural sliding mode control in discrete time for a SCARA robot arm. IEEE Lat. Am. Trans. 14(6), 2556–2564 (2016)
Hashem, Z.S.M., Khorashadizadeh, S., Fateh, M.M., Hadadzarif, M.: Optimal sliding mode control of a robot manipulator under uncertainty using PSO. Nonlinear Dyn. 84(4), 2227–2239 (2016)
Zhu, S.Q., Jin, X.L., Yao, B., Chen, Q.C., Pei, X., Pan, Z.Q.: Non-linear sliding mode control of the lower extremity exoskeleton based on human-robot cooperation. Int. J. Adv. Rob. Syst. 13(5), 1–10 (2016)
Zhu, S.Q., Chen, Q.C., Wang, X.Y., Liu, S.G.: Dynamic modelling using screw theory and nonlinear sliding mode control of serial robot. Int. J. Robot. Autom. 31(1), 63–75 (2016)
Ayman, A.K., Najib, E., Abdelaziz, H., Frdric, N., Janan, Z.: Type-2 fuzzy sliding mode control without reaching phase for nonlinear system. Eng. Appl. Artif. Intell. 24(1), 23–38 (2011)
Alouia, S., Pages, O., El Hajjaji, A., Chaari, K.Y.: Improved fuzzy sliding mode control for a class of MIMO nonlinear uncertain and perturbed systems. Appl. Soft Comput. J. 11(1), 820–826 (2011)
He, J., Luo, M.Z., Zhang, X.L., Ceccarelli, M., Fang, J., Zhao, J.H.: Adaptive fuzzy sliding mode control for redundant manipulators with varying payload. Ind. Robot 43(6), 665–676 (2016)
Acknowledgement
This work is supported by National Natural Science Foundation under Grant 51575407, 51575338, 51575412 and the UK Engineering and Physical Science Research Council under Grant EP/G041377/1. This support is greatly acknowledged.
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Du, F. et al. (2017). Simulation of 2-DOF Articulated Robot Control Based on Adaptive Fuzzy Sliding Mode Control. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_52
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DOI: https://doi.org/10.1007/978-3-319-65289-4_52
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