Hybrid Manipulator Running Trajectory Prediction Algorithm Based on PLC Fuzzy Control

  • Yunsheng ChenEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 279)


Aiming at the problem of multi-band motion and multi-joint inflection point of hybrid manipulator, the conventional trajectory prediction algorithm cannot satisfy the fast analysis and accurate control of motion trajectory. This paper proposes a hybrid manipulator running trajectory prediction algorithm based on PLC fuzzy control. Based on newton-andrews law, the dynamic model of hybrid manipulator was built, and the dynamics of hybrid manipulator was analyzed and the dynamic characteristics were determined. PLC fuzzy control unit is introduced, based on the kinematics characteristics of hybrid manipulator, the relevant input and output variables of PLC fuzzy control unit are determined, and the fuzzy strategy is implemented and analyzed. The construction of a hybrid manipulator based on fuzzy control is completed. The test data show that the proposed prediction algorithm is better than the conventional prediction algorithm, and the accuracy is improved by 57.42%, which is applicable to the prediction of the operation trajectory of the hybrid manipulator.


PLC fuzzy control Hybrid manipulator Trajectory Prediction algorithm 


  1. 1.
    Zhou, Y., Hu, D., Jin, R., Hu, J.: Application of fuzzy control based on PLC in ship rudder roll stabilization system. Mod. Electron. Tech. 39(2), 140–142 (2016)Google Scholar
  2. 2.
    Wang, P., Hong, Y., Huang, H., et al.: Application of fuzzy PID controller based on PLC in hot air drying oven. Food Mach. 32(12), 100–104 (2016)Google Scholar
  3. 3.
    Xu, Q., Yang, S., Yang, M.: Offline robot track intelligent optimization—based on improved differential evolution algorithm. Agric. Mech. Res. 39(2), 191–195 (2017)MathSciNetGoogle Scholar
  4. 4.
    Huang, H., Zhang, G., Yang, Y.: Dynamic modeling and coordinate motion trajectory optimization for underwater vehicle and manipulator system. J. Shanghai Jiaotong Univ. 50(9), 1437–1443 (2016)Google Scholar
  5. 5.
    Feng, D., Zhang, X., Zhang, X., et al.: RANSAC-based spatial circle fitting algorithm and it’s application on motion range detection of a manipulator. Opt. Tech. 14(2), 156–160 (2016)Google Scholar
  6. 6.
    Xu, J., Mei, J., Duan, X., et al.: A continuous trajectory planning transition algorithm for industrial robots. Chin. J. Eng. Des. 23(6), 537–543 (2016)Google Scholar
  7. 7.
    Guo-zhen, B.A.I., Peng-xiang, J.I.N.G.: Trajectory planning of delta manipulators based on modified gravitational search algorithm. Control Eng. Chin. 24(9), 1823–1828 (2017)Google Scholar
  8. 8.
    Zhang, L., Wei, P., Li, P., Wang, X., Liu, X.: Fabric grasp planning for multi-fingered dexterous hand based on neural network algorithm. J. Text. Res. 38(1), 132–139 (2017)Google Scholar
  9. 9.
    Tong, Z., Guo, R., Li, L., Lin, Y.: Study on trajectory controlling of hydraulic sampling joint manipulator. Mach. Des. Manuf. 5(11), 162–165 (2016)Google Scholar
  10. 10.
    Huang, Z., Xiang, Y., Li, Z., Lu, N.: Trajectory planning and design of control system for road cone automatic retractable manipulator. Chin. J. Constr. Mach. 15(4), 283–290 (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Guangzhou Huali Science and Technology Vocational CollegeGuangzhouChina

Personalised recommendations