IMPEDANCE CONTROL METHOD OF ROBOTIC ARM BASED ON RADIAL BASIS FUNCTION NEURAL NETWORK UNDER THE UNCERTAIN CONDITION OF ENVIRONMENTAL INFORMATION, 1-12.

Xingrui Li

References

  1. [1] Y. Fang, B. Luo, T. Zhao, D. He, B. Jiang, and Q.Liu, ST-SIGMA: Spatio-temporal semantics and interactiongraph aggregation for multi-agent perception and trajectoryforecasting, CAAI Transactions on Intelligence Technology,7(4), 2022, 744–757.
  2. [2] J. Zan, Research on robot path perception and optimizationtechnology based on whale optimization algorithm, Journal ofComputational and Cognitive Engineering, 1(4), 2022, 201–208.
  3. [3] P. Dey and D.K. Jana, Evaluation of the convincing abilitythrough presentation skills of pre-service management wizardsusing AI via T2 linguistic fuzzy logic, Journal of Computationaland Cognitive Engineering, 2(2), 2022, 133–142.
  4. [4] D.H.T. Nguyen, R.H. Utama, K.C. Tjandra, P. Suwannakot,E.Y. Du, M. Kavallaris, R.D. Tilley, and J.J. Gooding, Tuningthe mechanical properties of multiarm RAFT-based blockcopolyelectrolyte hydrogels, Biomacromolecules, 24(1), 2023,57–68.
  5. [5] M.R. Sarifin, M. Nor, S. Ali, N. Ameera, and I.S. Hamzah,Categories for non-compliance of movement control order inmalaysia: A review through online news report, Psychology(Savannah, Ga.), 57(8), 2020, 263–290.
  6. [6] C.M. Murea and D. Tiba, Topological optimization and minimalcompliance in linear elasticity, Evolution Equations and ControlTheory, 9(4), 2020, 1115–1131.
  7. [7] Z. Chen, Research on internet security situation awarenessprediction technology based on improved RBF neuralnetwork algorithm, Journal of Computational and CognitiveEngineering, 1(3), 2022, 103–108.
  8. [8] I.Z. Chen and J.T. Chang, Applying a 6-axis mechanicalarm combine with computer vision to the research of objectrecognition in plane inspection, Journal of Artificial Intelligenceand Capsule Networks, 2(2), 2020, 77–99.
  9. [9] T. Xu, H. Zhou, S. Tan, Z. Li, X. Ju, and Y. Peng, Mechanicalarm obstacle avoidance path planning based on improvedartificial potential field method, Industrial Robot, 49(2), 2022,271–279.
  10. [10] A.T. Sadiq, F.A. Raheem, and N.A.F. Abbas, Ant colony algo-rithm improvement for robot arm path planning optimizationbased on D strategy, International Journal of Mechanical &Mechatronics Engineering, 21(1), 2021, 96–111.
  11. [11] H.K. Sharaf, M.R. Ishak, S.M. Sapuan, N. Yidris, and A.Fattahi, Experimental and numerical investigation of themechanical behavior of full-scale wooden cross arm in thetransmission towers in terms of load-deflection test, Journal ofMaterials Research and Technology, 9(4), 2020, 7937–7946.
  12. [12] Z. Rui, Y. Qingjun, C. Chen, C. Jiang, C. Li, and Y. Wang,Force-based active compliance control of hydraulic quadrupedrobot, International Journal of Fluid Power, 22(2), 2021,147–172.
  13. [13] T. Wang, H. Nian, and Z. Zhu, Hybrid virtual impedance-basedcontrol strategy for dfig in hybrid wind farm to disperse negativesequence current during network unbalance, IET RenewablePower Generation, 14(12), 2020, 2268–2277.
  14. [14] M. Hanafusa and J. Ishikawa, Mechanical impedance control ofcooperative robot during object manipulation based on externalforce estimation using recurrent neural network, UnmannedSystems, 8(3), 2020, 239–251.
  15. [15] M. Mokhtari, M. Taghizadeh, and P.G. Ghanbari, Faulttolerant control based on backstepping nonsingular terminalintegral sliding mode and impedance control for a lowerlimb exoskeleton. Proceedings of the Institution of MechanicalEngineers, Part C: Journal of Mechanical Engineering Science,236(6), 2022, 2698–2713.
  16. [16] J. Chen, Q. Chen, and H. Yang, Additive manufacturing ofa continuum topology-optimized palletizing manipulator arm,Mechanical Sciences, 12(1), 2021, 289–304.
  17. [17] H. Yang, S. Wu, and G. Huang, Fuzzy neural network controlfor mechanical arm based on adaptive friction compensation,Journal of Vibroengineering, 22(5), 2020, 1099–1112.
  18. [18] A. Kumar and R. Sharma, Linguistic Lyapunov reinforcementlearning control for robotic manipulators, Neurocomputing,272, 2017, 84–95.
  19. [19] A. Kumar and R. Sharma, Fuzzy Lyapunov reinforcementlearning for non linear systems, ISA Transactions, 67, 2017,151–159.
  20. [20] A. Kumar and R. Sharma, Neural/fuzzy self learning Lyapunovcontrol for non linear systems, International Journal ofInformation Technology, 14, 2022, 229–242
  21. [21] Z. Lu, P. Huang, and Z. Liu, High-gain nonlinear observer-based impedance control for deformable object cooperativeteleoperation with nonlinear contact model, InternationalJournal of Robust and Nonlinear Control, 30(4), 2020,1329–1350.
  22. [22] J. Dong, and J. Xu, Physical human-robot interaction forcecontrol method based on adaptive variable impedance, Journalof the Franklin Institute, 357(12), 2020, 7864–7878.
  23. [23] X. Zhang, T. Sun, and D. Deng, Neural approximation-basedadaptive variable impedance control of robots, Transactionsof the Institute of Measurement and Control, 42(13), 2020,2589–2598.11
  24. [24] A. Kumar, Reinforcement learning: Application and advancestowards stable control strategies, Mechatronic Systems andControl, 51(1), 2023, 53–57.
  25. [25] A.K. Yadav, V. Kumar, A.S. Pandey, S.M. Tripathi, and A.Kumar, Wind farm integrated fuzzy logic-based facts controlledpower system stability analysis, Mechatronic Systems andControl, 51(4), 2023, 172–181.
  26. [26] A. Kumar, R. Sharma, and P. Vershey, Lyapunov fuzzy Markovgame controller for two link robotic manipulator, Journal ofIntelligent & Fuzzy Systems, 34(3), 2018, 1479–1490.

Important Links:

Go Back