REFERENCES

1. Liu WL, Yin BL, Yan BB. A survey on the exoskeleton rehabilitation robot for the lower limbs. In: 2016 2nd International Conference on Control, Automation and Robotics (ICCAR); 2016 Apr 28-30; Hong Kong, China. IEEE; 2016. pp. 90–94.

2. Meng W, Liu Q, Zhou ZD, Ai QS, Sheng B, Xie SQ. Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics 2015;31:132-45.

3. Han J, Yang SY, Xia L, Chen YH. Deterministic adaptive robust control with a novel optimal gain design approach for a fuzzy 2-DOF lower limb exoskeleton robot system. IEEE Trans Fuzzy Syst 2021;29:2373-87.

4. Sun W, Lin JW, Su SF, Wang N, Er MJ. Reduced adaptive fuzzy decoupling control for lower limb exoskeleton. IEEE Trans Cybern 2021;51:1099-109.

5. Long Y, Du ZJ, Cong L, Wang WD, Zhang ZM, Dong W. Active disturbance rejection control based human gait tracking for lower extremity rehabilitation exoskeleton. ISA Trans 2017;67:389-97.

6. Asl HJ, Narikiyo T, Kawanishi M. Neural network-based bounded control of robotic exoskeletons without velocity measurements. Contr Eng Pract 2018;80:94-104.

7. Torres FJ, Guerrero GV, García CD, Gomez JF, Adam M and Escobar RF. Master-slave synchronization of robot manipulators driven by induction motors. IEEE Latin Am Trans 2016;14:3986-91.

8. Ahmed S, Wang HP, Tian Y. Robust adaptive fractional-order terminal sliding mode control for lower-limb exoskeleton. Asian J of Contr 2019;21:473-82.

9. Narayan J, Abbas M, Patel B, Dwivedy SK. A Singularity-free terminal sliding mode control of an uncertain paediatric exoskeleton system. In: 2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET); 2022 Mar 22-25; Hammamet, Tunisia. IEEE; 2022. pp. 198–203.

10. Cao SB, Cao GZ, Zhang YP, Ling ZQ, He BB, Huang SD. Fast-terminal sliding mode control based on dynamic boundary layer for lower limb exoskeleton rehabilitation robot. In: 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER); 2021 July 27-31; Jiaxing, China. IEEE; 2021. pp. 453–458.

11. Chen SB, Beigi A, Yousefpour A, et al. Recurrent neural network-based robust nonsingular sliding mode control with input saturation for a non-holonomic spherical robot. IEEE Access 2020;8:188441-53.

12. Fei JT, Feng ZL. Fractional-order finite-time super-twisting sliding mode control of micro gyroscope based on double-loop fuzzy neural network. IEEE Trans Syst Man Cybern, Syst 2021;51:7692-706.

13. Wang YY, Gu LY, Xu YH, Cao XX. Practical tracking control of robot manipulators with continuous fractional-order nonsingular terminal sliding mode. IEEE Trans Ind Electron 2016;63:6194-204.

14. Yang Y, Chen YQ, Chu YZ, Wang Y, Liang Q. Fractional order adaptive sliding mode controller for permanent magnet synchronous motor. In: 2016 35th Chinese Control Conference (CCC); 2016 July 27-29; Chengdu, China. IEEE; 2016. pp. 3412–3416.

15. Lavín-Delgado JE, Chávez-Vázquez S, Gómez-Aguilar JF, Alassafi MO, Alsaadi FE, Ahmad AM. Intelligent Neural Integral Sliding-mode Controller for a space robotic manipulator mounted on a free-floating satellite. Adv Space Res 2022; Epub ahead of print.

16. Lavín-Delgado JE, Beltrán ZZ, Gómez-Aguilar JF, Pérez-Careta E. Controlling a quadrotor UAV by means of a fractional nested saturation control. Adv Space Res 2022; Epub ahead of print.

17. Li JF, Jahanshahi H, Kacar S, et al. On the variable-order fractional memristor oscillator: Data security applications and synchronization using a type-2 fuzzy disturbance observer-based robust control. Chaos, Solitons & Fractals 2021;145:110681.

18. Wang YL, Jahanshahi H, Bekiros S, Bezzina F, Chu YM, Aly AA. Deep recurrent neural networks with finite-time terminal sliding mode control for a chaotic fractional-order financial system with market confidence. Chaos, Solitons & Fractals 2021;146:110881.

19. Xiong PY, Jahanshahi H, Alcaraz R, Chu YM, Gómez-Aguilar JF, Alsaadi FE. Spectral entropy analysis and synchronization of a multi-stable fractional-order chaotic system using a novel neural network-based chattering-free sliding mode technique. Chaos, Solitons & Fractals 2021;144:110576.

20. Li SH, Du HB, Yu XH. Discrete-time terminal sliding mode control systems based on euler's discretization. IEEE Trans Automat Contr 2014;59:546-52.

21. Chen B, Hu GQ, Ho DWC, Yu L. Distributed Estimation and Control for Discrete Time-Varying Interconnected Systems. IEEE Trans Automat Contr 2022;67:2192-207.

22. Sun GH, Ma ZQ, Yu JY. Discrete-time fractional order terminal sliding mode tracking control for linear motor. IEEE Trans Ind Electron 2018;65:3386-94.

23. Ajjanaromvat N, Parnichkun M. Trajectory tracking using online learning LQR with adaptive learning control of a leg-exoskeleton for disorder gait rehabilitation. Mechatronics 2018;51:85-96.

24. Neuman CP, Tourassis VD. Discrete dynamic robot models. IEEE Trans Syst, Man, Cybern 1985;SMC-15:193-204.

25. Zhou Y, Hu ZY, Sun Z, Wang T, Chen B. Covariance intersection fusion approach for gait estimation of lower limb rehabilitation Exoskeleton Robot. In: 2022 5th International Symposium on Autonomous Systems (ISAS); 2022 April 08-10; Hangzhou, China. IEEE; 2022. pp. 1–6.

26. Gao WB, Wang YF, A HMF. Discrete-time variable structure control systems. IEEE Trans Ind Electron 1995;42:117-22.

27. Wang Z, Li SH, Li Q. Discrete-time fast terminal sliding mode control design for DC-DC buck converters with mismatched disturbances. IEEE Trans Ind Inf 2020;16:1204-13.

28. Delp SL, Anderson FC, Arnold AS, et al. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 2007;54:1940-50.

29. Seth A, Hicks JL, Uchida TK, et al. OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput Biol 2018;14:e1006223.

30. Mi WM, Zhang T. Fuzzy variable impedance adaptive robust control algorithm of exoskeleton robots. In: 2019 Chinese Control Conference (CCC); 2019 July 27-30; Guangzhou, China. IEEE; 2019. pp. 4302–7.