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Research Articles

Adaptive finite-time optimised impedance control for robotic manipulators with state constraints

ORCID Icon, , ORCID Icon, &
Pages 2040-2058 | Received 05 Jun 2022, Accepted 07 May 2023, Published online: 23 May 2023

References

  • An, S., Chen, M., Wang, H., & Wu, L. (2021). Fast finite-time dynamic surface tracking control of a single-joint manipulator system with prescribed performance. International Journal of Systems Science, 52(8), 1551–1563. https://doi.org/10.1080/00207721.2020.1864506
  • Bai, Y., Cao, Y., & Li, T. (2019). Optimized backstepping design for ship course following control based on actor-critic architecture with input saturation. IEEE Access, 7, 73516–73528. https://doi.org/10.1109/ACCESS.2019.2919249
  • Brahmi, B., Driscoll, M., El Bojairami, I. K., Saad, M., & Brahmi, A. (2021). Novel adaptive impedance control for exoskeleton robot for rehabilitation using a nonlinear time-delay disturbance observer. ISA Transactions, 108, 381–392. https://doi.org/10.1016/j.isatra.2020.08.036
  • Cao, F., & Liu, J. (2018). Optimal trajectory control for a two-link rigid-flexible manipulator with ODE-PDE model. Optimal Control Applications and Methods, 39(4), 1515–1529. https://doi.org/10.1002/oca.2423
  • Chen, Z., Yang, X., & Liu, X. (2019). RBFNN-based nonsingular fast terminal sliding mode control for robotic manipulators including actuator dynamics. Neurocomputing, 362, 72–82. https://doi.org/10.1016/j.neucom.2019.06.083
  • Ding, S., Peng, J., Zhang, H., & Wang, Y. (2021). Neural network-based adaptive hybrid impedance control for electrically driven flexible-joint robotic manipulators with input saturation. Neurocomputing, 458, 99–111. https://doi.org/10.1016/j.neucom.2021.05.095
  • Furtado, G. P., Americano, P. P., & Forner-Cordero, A. (2020). Impedance control as an optimal control problem: A novel formulation of impedance controllers as a subcase of optimal control. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42(10), 513. https://doi.org/10.1007/s40430-020-02586-x
  • Hamedani, M. H., Zekri, M., Sheikholeslam, F., Selvaggio, M., Ficuciello, F., & Siciliano, B. (2021). Recurrent fuzzy wavelet neural network variable impedance control of robotic manipulators with fuzzy gain dynamic surface in an unknown varied environment. Fuzzy Sets and Systems, 416, 1–26. https://doi.org/10.1016/j.fss.2020.05.001
  • He, W., Dong, Y., & Sun, C. (2016). Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(3), 334–344. https://doi.org/10.1109/TSMC.2015.2429555
  • He, W., Xue, C., Yu, X., Li, Z., & Yang, C. (2020). Admittance-Based controller design for physical human–robot interaction in the constrained task space. IEEE Transactions on Automation Science and Engineering, 17(4), 1937–1949. https://doi.org/10.1109/TASE.2020.2983225
  • Hogan, N. (1985). Impedance control: An approach to manipulation: Part I—theory. Journal of Dynamic Systems, Measurement, and Control, 107(1), 1–7. https://doi.org/10.1115/1.3140702
  • Huang, X., Lin, W., & Yang, B. (2005). Global finite-time stabilization of a class of uncertain nonlinear systems. Automatica, 41(5), 881–888. https://doi.org/10.1016/j.automatica.2004.11.036
  • Khan, A. T., Li, S., & Zhou, X. (2021). Trajectory optimization of 5-link biped robot using beetle antennae search. IEEE Transactions on Circuits and Systems II: Express Briefs, 68(10), 3276–3280. https://doi.org/10.1109/TCSII.2021.3062639
  • Kou, B., Ren, D., & Guo, S. (2022). Geometric parameter identification of medical robot based on improved beetle antennae search algorithm. Bioengineering, 9(2), 58. https://doi.org/10.3390/bioengineering9020058
  • Li, D., & Li, D. (2018). Adaptive neural tracking control for an uncertain state constrained robotic manipulator with unknown time-varying delays. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(12), 2219–2228. https://doi.org/10.1109/TSMC.2017.2703921
  • Li, G., Yu, J., & Chen, X. (2021a). Adaptive fuzzy neural network command filtered impedance control of constrained robotic manipulators with disturbance observer. IEEE Transactions on Neural Networks and Learning Systems, 1–10. https://doi.org/10.1109/TNNLS.2021.3113044
  • Li, S., Chen, S., & Liu, B. (2013a). Accelerating a recurrent neural network to finite-time convergence for solving time-varying Sylvester equation by using a sign-Bi-power activation function. Neural Processing Letters, 37(2), 189–205. https://doi.org/10.1007/s11063-012-9241-1
  • Li, S., Li, Y., & Wang, Z. (2013b). A class of finite-time dual neural networks for solving quadratic programming problems and its -winners-take-all application. Neural Networks, 39, 27–39. https://doi.org/10.1016/j.neunet.2012.12.009
  • Li, X., & Li, Y. (2021). Neural networks optimized learning control of state constraints systems. Neurocomputing, 453, 512–523. https://doi.org/10.1016/j.neucom.2020.10.034
  • Li, Z., Zhai, J., & Karimi, H. R. (2021b). Adaptive finite-time super-twisting sliding mode control for robotic manipulators with control backlash. International Journal of Robust and Nonlinear Control, 31(17), 8537–8550. https://doi.org/10.1002/rnc.5744
  • Liu, X., Ge, S. S., Zhao, F., & Mei, X. (2021). Optimized impedance adaptation of robot manipulator interacting with unknown environment. IEEE Transactions on Control Systems Technology, 29(1), 411–419. https://doi.org/10.1109/TCST.2020.2971944
  • Liu, Y., Zhu, Q., & Wen, G. (2020). Adaptive tracking control for perturbed strict-feedback nonlinear systems based on optimized backstepping technique. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 853–865. https://doi.org/10.1109/TNNLS.2019.2931183
  • Nazmara, G., Fateh, M. M., & Ahmadi, S. M. (2020). Exponentially convergence for the regressor-free adaptive fuzzy impedance control of robots by gradient descent algorithm. International Journal of Systems Science, 51(11), 1883–1904. https://doi.org/10.1080/00207721.2020.1780513
  • Peng, G., Chen, C. L. P., & Yang, C. (2021). Neural networks enhanced optimal admittance control of robot-environment interaction using reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 33(9), 4551–4561. https://doi.org/10.1109/TNNLS.2021.3131661
  • Peng, J., Ding, S., Yang, Z., & Xin, J. (2020). Adaptive neural impedance control for electrically driven robotic systems based on a neuro-adaptive observer. Nonlinear Dynamics, 100(2), 1359–1378. https://doi.org/10.1007/s11071-020-05569-8
  • Raibert, M. H., & Craig, J. J. (1981). Hybrid position/force control of manipulators. Journal of Dynamic Systems, Measurement, and Control, 103(2), 126–133. https://doi.org/10.1115/1.3139652
  • Razmjooei, H., Shafiei, M. H., Abdi, E., & Yang, C. (2020). A novel robust finite-time tracking control of uncertain robotic manipulators with disturbances. Journal of Vibration and Control, 28(5-6), 719–731. https://doi.org/10.1177/1077546320982449
  • Sai, H., Xu, Z., Li, Y., & Wang, K. (2021). Adaptive nonsingular fast terminal sliding mode impedance control for uncertainty robotic manipulators. International Journal of Precision Engineering and Manufacturing, 22(12), 1947–1961. https://doi.org/10.1007/s12541-021-00589-9
  • Si, W., Qi, L., Hou, N., & Dong, X. (2019). Finite-time adaptive neural control for uncertain nonlinear time-delay systems with actuator delay and full-state constraints. International Journal of Systems Science, 50(4), 726–738. https://doi.org/10.1080/00207721.2019.1567869
  • Tong, M., & Wang, H. (2022). Observer-based adaptive fuzzy finite-time tracking control of switched nonlinear systems. International Journal of Systems Science, 53(11), 2407–2420. https://doi.org/10.1080/00207721.2022.2053231
  • Van, M., Mavrovouniotis, M., & Ge, S. S. (2019). An adaptive backstepping nonsingular fast terminal sliding mode control for Robust Fault tolerant control of robot manipulators. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(7), 1448–1458. https://doi.org/10.1109/TSMC.2017.2782246
  • Wang, C., Li, Y., Ge, S. S., & Lee, T. H. (2015). Optimal critic learning for robot control in time-varying environments. IEEE Transactions on Neural Networks and Learning Systems, 26(10), 2301–2310. https://doi.org/10.1109/TNNLS.2014.2378812
  • Wang, F., Chen, B., Liu, X., & Lin, C. (2018). Finite-time adaptive fuzzy tracking control design for nonlinear systems. IEEE Transactions on Fuzzy Systems, 26(3), 1207–1216. https://doi.org/10.1109/TFUZZ.2017.2717804
  • Wang, N., Gao, Y., Zhao, H., & Ahn, C. K. (2021). Reinforcement learning-based optimal tracking control of an unknown unmanned surface vehicle. IEEE Transactions on Neural Networks and Learning Systems, 32(7), 3034–3045. https://doi.org/10.1109/TNNLS.2020.3009214
  • Wen, G., Chen, C. L. P., & Ge, S. S. (2021). Simplified optimized backstepping control for a class of nonlinear strict-feedback systems with unknown dynamic functions. IEEE Transactions on Cybernetics, 51(9), 4567–4580. https://doi.org/10.1109/TCYB.2020.3002108
  • Wen, G., Ge, S. S., Chen, C. L. P., Tu, F., & Wang, S. (2019). Adaptive tracking control of surface vessel using optimized backstepping technique. IEEE Transactions on Cybernetics, 49(9), 3420–3431. https://doi.org/10.1109/TCYB.2018.2844177
  • Wen, G., Ge, S. S., & Tu, F. (2018). Optimized backstepping for tracking control of strict-feedback systems. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3850–3862. https://doi.org/10.1109/TNNLS.2018.2803726
  • Wu, X., Li, Z., Kan, Z., & Gao, H. (2020). Reference trajectory reshaping optimization and control of robotic exoskeletons for human–robot co-manipulation. IEEE Transactions on Cybernetics, 50(8), 3740–3751. https://doi.org/10.1109/TCYB.2019.2933019
  • Xia, J., Zhang, J., Sun, W., Zhang, B., & Wang, Z. (2019). Finite-time adaptive fuzzy control for nonlinear systems with full state constraints. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(7), 1541–1548. https://doi.org/10.1109/TSMC.2018.2854770
  • Xie, Y., Yan, Y., Shi, Z., Wu, X., Cui, H., & Zhang, Z. (2020). Adaptive optimal tracking control for multi-joint manipulator on space robot. Optimal Control Applications and Methods, 41(6), 1995–2007. https://doi.org/10.1002/oca.2613
  • Yang, Z., Peng, J., & Liu, Y. (2019). Adaptive neural network force tracking impedance control for uncertain robotic manipulator based on nonlinear velocity observer. Neurocomputing, 331, 263–280. https://doi.org/10.1016/j.neucom.2018.11.068
  • Yu, S., Yu, X., Shirinzadeh, B., & Man, Z. (2005). Continuous finite-time control for robotic manipulators with terminal sliding mode. Automatica, 41(11), 1957–1964. https://doi.org/10.1016/j.automatica.2005.07.001
  • Zhai, J., & Xu, G. (2021). A novel non-singular terminal sliding mode trajectory tracking control for robotic manipulators. IEEE Transactions on Circuits and Systems II: Express Briefs, 68(1), 391–395. https://doi.org/10.1109/TCSII.2020.2999937
  • Zhang, S., Dong, Y., Ouyang, Y., Yin, Z., & Peng, K. (2018a). Adaptive neural control for robotic manipulators with output constraints and uncertainties. IEEE Transactions on Neural Networks and Learning Systems, 29(11), 5554–5564. https://doi.org/10.1109/TNNLS.2018.2803827
  • Zhang, T., & Zhang, A. (2020). Robust finite-time tracking control for robotic manipulators with time delay estimation. Mathematics, 8(2), 165. https://doi.org/10.3390/math8020165
  • Zhang, Y., Li, S., & Jiang, X. (2018b). Near-Optimal control without solving HJB equations and its applications. IEEE Transactions on Industrial Electronics, 65(9), 7173–7184. https://doi.org/10.1109/TIE.2018.2793233
  • Zhang, Y., Li, S., Kadry, S., & Liao, B. (2019a). Recurrent neural network for kinematic control of redundant manipulators with periodic input disturbance and physical constraints. IEEE Transactions on Cybernetics, 49(12), 4194–4205. https://doi.org/10.1109/TCYB.2018.2859751
  • Zhang, Y., Li, S., & Liu, X. (2018c). Neural network-based model-free adaptive near-optimal tracking control for a class of nonlinear systems. IEEE Transactions on Neural Networks and Learning Systems, 29(12), 6227–6241. https://doi.org/10.1109/TNNLS.2018.2828114
  • Zhang, Y., Li, S., & Zhou, X. (2019b). Recurrent-neural-network-based velocity-level redundancy resolution for manipulators subject to a joint acceleration limit. IEEE Transactions on Industrial Electronics, 66(5), 3573–3582. https://doi.org/10.1109/TIE.2018.2851960
  • Zhao, Z., He, W., & Ge, S. S. (2014). Adaptive neural network control of a fully actuated marine surface vessel with multiple output constraints. IEEE Transactions on Control Systems Technology, 22(4), 1536–1543. https://doi.org/10.1109/TCST.2013.2281211

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