MULTI-STEP OPTIMAL PREDICTIVE CONTROL FOR PATH CORRECTION OF THE AGV DRIVEN BY HUB MOTORS, 304-315.

Xiaojun Wu,∗ Yan Li,∗ Huibo Jia,∗ Guangqiang Ma,∗ and Ying Zhang∗

References

  1. [1] R.M.T.R. Ismail, N.D. That, and Q.P. Ha, Modelling androbust trajectory following for offshore container crane systems,Automation in Construction, 59, 2015, 179–187.
  2. [2] F.A. Cheein, Intelligent sampling technique for path trackingcontrollers, IEEE Transactions on Control Systems Technology,24(2), 2016, 747–755.313
  3. [3] A. Basci and A. Derdiyok, Real-time velocity and directionangle control of an automated guided vehicle, InternationalJournal of Robotics & Automation, 29(3), 2014, 227–233.
  4. [4] W. He, L. Zhijun, and C.L. Philip Chen, A survey of human-centered intelligent robots: Issues and challenges, IEEE/CAAJournal of Automatica Sinica, 4(4), 2017, 602–609.
  5. [5] N. Hung, J.S. Im, S.K. Jeong, H.K. Kim, and S.B. Kim, Designof a sliding mode controller for an automatic guided vehicleand its implementation, International Journal of Control,Automation and Systems, 8(1), 2010, 81–90.
  6. [6] E. Kim, J. Kim, and M. Sunwoo, Model predictive controlstrategy for smooth path tracking of autonomous vehicleswith steering actuator dynamics, International Journal ofAutomotive Technology, 15(7), 2014, 1155–1164.
  7. [7] L. Ssebazza and Y.J. Pan, DGPS-based localization and pathfollowing approach for outdoor wheeled mobile robots, In-ternational Journal of Robotics & Automation, 30(1), 2015,13–25.
  8. [8] T. Huang, P. Yang, K.M. Yang, and Y. Zhu, Navigation ofmobile robot in unknown environment based on T-sneuro-fuzzy system, International Journal of Robotics & Automation,30(4), 2015, 384–396.
  9. [9] H. We and D. Yiting, Adaptive fuzzy neural network control fora constrained robot using impedance learning, IEEE Transac-tions on Neural Networks and Learning Systems, 29(4), 2018,1174–1186.
  10. [10] Z.C. Cao, Y.T. Zhao, and Q.D. Wu, Path tracking control fora wheeled mobile robot by integrating neural dynamics withadaptive approach, Control Theory & Applications, 27(12),2010, 1717–1723.
  11. [11] X. Niu, G. Gao, Z. Bao, and H. Zhou, Path tracking ofmobile robots for greenhouse spraying controlled by slidingmode variable structure, Transactions of the Chinese Societyof Agricultural Engineering, 29(2), 2013, 9–16.
  12. [12] Z. Liu, W. Zhang, Z. Lu, W. Zheng, G. Mu, and X. Cheng,Design on trajectory tracking controller of agricultural vehiclesunder disturbances, Transactions of the Chinese Society forAgricultural Machinery, 49(12), 2018, 378–386.
  13. [13] L. Jiang and J. Yang, Path tracking of automatic parkingsystem based on sliding mode control, Transactions of theChinese Society for Agricultural Machinery, 50(2), 2019, 356–364.
  14. [14] Z. Zhao, L. Zhou, and Q. Zhu, Preview distance adaptiveoptimization for the path tracking control of unmanned vehicle,Journal of Mechanical Engineering, 54(24), 2018, 166–173.
  15. [15] Q. Meng, R. Qiu, M. Zhang, G. Liu, Z. Zhang, and M.Xiang, Navigation system of agricultural vehicle based on fuzzylogic controller with improved particle swarm optimizationalgorithm, Transactions of the Chinese Society for AgriculturalMachinery, 46(3), 2015, 29–36.
  16. [16] S. Ghaffari and M.R. Homaeinezhad, Autonomous path fol-lowing by fuzzy adaptive curvature-based point selection algo-rithm for four-wheel-steering car-like mobile robot, Proceedingsof the Institution of Mechanical Engineers Part C-Journal ofMechanical Engineering Science, 232(15), 2018, 2655–2665.
  17. [17] W. He, Y. Ouyang, and J. Hong, Vibration control of a flexiblerobotic manipulator in the presence of input deadzone, IEEETransactions on Industrial Informatics, 13(1), 2017, 48–59.
  18. [18] H. Siljak, Inverse matching-based mobile robot following algo-rithm using fuzzy logic, International Journal of Robotics &Automation, 29(4), 2014, 369–377.
  19. [19] W. He and S.S. Ge, Cooperative control of a nonuniformgantry crane with constrained tension, Automatica, 66, 2016,146–154.
  20. [20] K. Kim, K. Hwang, and H. Kim, Study of an adaptivefuzzy algorithm to control a rectangular-shaped unmannedsurveillance flying car, Journal of Mechanical Science andTechnology, 27(8), 2013, 2477–2486.
  21. [21] X. Qian, L. Zhu, P. Lou and H. Zhang, Optimal path trackingcontrol method of omni-directional mobile AGV based on posestate, Transactions of the Chinese Society for AgriculturalMachinery, 49(4), 2018, 20–26.
  22. [22] E. Kayacan, E. Kayacan, H. Ramon, O. Kaynak and W. Saeys,Towards agrobots: Trajectory control of an autonomous tractorusing type-2 fuzzy logic controllers, IEEE-ASME Transactionson Mechatronics, 20(1), 2015, 287–298.
  23. [23] V.F. Carida, O. Morandin Jr., and C.C.M. Tuma, Approachesof fuzzy systems applied to an AGV dispatching system ina FMS, International Journal of Advanced ManufacturingTechnology, 79(1–4), 2015, 615–625.
  24. [24] H. Fazlollahtabar and M. Saidi-Mehrabad, Methodologies tooptimize automated guided vehicle scheduling and routingproblems: A review study, Journal of Intelligent & RoboticSystems, 77(3–4), 2015, 525–545.
  25. [25] J. Shouwen, L. Keqiang and M. Lixin, Design of a new type ofAGV based on computer vision, Chinese Journal of MechanicalEngineering, 17(1), 2004, 97–101.
  26. [26] R. Bhattacharya, Robust LQR design for systems with proba-bilistic uncertainty, International Journal of Robust and Non-linear Control, 29(10), 2019, 3217–3237.
  27. [27] Z. Wang, H. Yue, C. Li and J. Liu, Optimal control ofwheeled mobile robot trajectory tracking, Mechanical Scienceand Technology, 25(1), 2006, 21–23.
  28. [28] L. Cheng, Z. Fuyu, H. Suxia and P. Jinfeng, A referencemodel decoupling method for multivariable systems, ControlEngineering of China, 16(1), 2009, 12–15.

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