Similar articles

Abstract: Accurate acceleration acquisition is a critical issue in the robotic exoskeleton system, but it is difficult to directly obtain the acceleration via the existing sensing systems. The existing algorithm-based acceleration acquisition methods put more attention on finite-time convergence and disturbance suppression but ignore the error constraint and initial state irrelevant techniques. To this end, a novel radical bias function neural network (RBFNN) based fixed-time reconstruction scheme with error constraints is designed to realize high-performance acceleration estimation. In this scheme, a novel exponential-type barrier Lyapunov function is proposed to handle the error constraints. It also provides a unified and concise Lyapunov stability-proof template for constrained and non-constrained systems. Moreover, a fractional power sliding mode control law is designed to realize fixed-time convergence, where the convergence time is irrelevant to initial states or external disturbance, and depends only on the chosen parameters. To further enhance observer robustness, an RBFNN with the adaptive weight matrix is proposed to approximate and attenuate the completely unknown disturbances. Numerical simulation and human subject experimental results validate the unique properties and practical robustness.

基于神经网络的固定时间约束下外骨骼机器人加速度重构方法

[1]Abooee A, Moravej Khorasani M, Haeri M, 2017. Finite time control of robotic manipulators with position output feedback. Int J Robust Nonl Contr, 27(16):2982-2999.

[2]Aguirre-Ollinger G, Colgate JE, Peshkin MA, et al., 2007. Active-impedance control of a lower-limb assistive exoskeleton. 10^th Int Conf on Rehabilitation Robotics, p.188-195.

[3]Chen Q, Cheng H, Yue C, et al., 2018. Dynamic balance gait for walking assistance exoskeleton. Appl Bion Biomech, 2018:7847014.

[4]Chen S, Chen Z, Yao B, et al., 2017. Adaptive robust cascade force control of 1-DOF hydraulic exoskeleton for human performance augmentation. IEEE/ASME Trans Mech, 22(2):589-600.

[5]Dong TY, Zhang XL, Liu T, 2018. Artificial muscles for wearable assistance and rehabilitation. Front Inform Technol Electron Eng, 19(11):1303-1315.

[6]Fei J, Ding H, 2012. Adaptive sliding mode control of dynamic system using RBF neural network. Nonl Dynam, 70(2):1563-1573.

[7]He S, Lin D, 2018. Reliable spacecraft rendezvous without velocity measurement. Acta Astron, 144:52-60.

[8]He Y, Li N, Wang C, et al., 2019. Development of a novel autonomous lower extremity exoskeleton robot for walking assistance. Front Inform Technol Electron Eng, 20(3):318-329.

[9]Hua CC, Yang Y, Guan X, 2013. Neural network-based adaptive position tracking control for bilateral teleoperation under constant time delay. Neurocomputing, 113:204-212.

[10]Huo WG, Mohammed S, Amirat Y, et al., 2016. Active impedance control of a lower limb exoskeleton to assist sit-to-stand movement. Proc IEEE Int Conf on Robotics and Automation, p.3530-3536.

[11]Kang I, Hsu H, Young A, 2019. The effect of hip assistance levels on human energetic cost using robotic hip exoskeletons. IEEE Robot Autom Lett, 4(2):430-437.

[12]Kazerooni H, Racine J, Huang LH, et al., 2005. On the control of the Berkeley lower extremity exoskeleton (BLEEX). Proc IEEE Int Conf on Robotics and Automation, p.4353-4360.

[13]Kim H, Shin YJ, Kim J, 2017. Design and locomotion control of a hydraulic lower extremity exoskeleton for mobility augmentation. Mechatronics, 46:32-45.

[14]Kim J, Heimgartner R, Lee G, et al., 2018. Autonomous and portable soft exosuit for hip extension assistance with online walking and running detection algorithm. Proc IEEE Int Con on Robotics and Automation, p.5473-5480.

[15]Kim J, Lee G, Heimgartner R, et al., 2019. Reducing the metabolic rate of walking and running with a versatile, portable exosuit. Science, 365(6454):668-672.

[16]Kuo CH, Yudha AP, Mohapatra SK, 2018. Force sensorless compliance control of a lower-limb exoskeleton robot. Int J Autom Smart Technol, 8(1):51-60.

[17]Li S, Yang J, Chen WH, et al., 2011. Generalized extended state observer based control for systems with mismatched uncertainties. IEEE Trans Ind Electron, 59(12):4792-4802.

[18]Long Y, Du ZJ, Wang WD, et al., 2018. Physical human-robot interaction estimation based control scheme for a hydraulically actuated exoskeleton designed for power amplification. Front Inform Technol Electron Eng, 19(9):1076-1085.

[19]Luenberger D, 1966. Observers for multivariable systems. IEEE Trans Autom Contr, 11(2):190-197.

[20]Nagarajan U, Aguirre-Ollinger G, Goswami A, 2016. Integral admittance shaping: a unified framework for active exoskeleton control. Robot Auton Syst, 75:310-324.

[21]Polyakov A, 2011. Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Trans Autom Contr, 57(8):2106-2110.

[22]Seo K, Kim K, Park YJ, et al., 2018. Adaptive oscillator-based control for active lower-limb exoskeleton and its metabolic impact. Proc IEEE Int Conf on Robotics and Automation, p.6752-6758.

[23]Shtessel Y, Edwards C, Fridman L, et al., 2014. Sliding Mode Control and Observation. Springer, Berlin, Germany.

[24]Tan CP, Yu X, Man Z, 2010. Terminal sliding mode observers for a class of nonlinear systems. Automatica, 46(8):1401-1404.

[25]Tanghe K, Aertbeliën E, Vantilt J, et al., 2018. Realtime delayless estimation of derivatives of noisy sensor signals for quasi-cyclic motions with application to joint acceleration estimation on an exoskeleton. IEEE Robot Autom Lett, 3(3):1647-1654.

[26]Wang ZW, Liang B, Wang XQ, 2018. Chattering-free fixed-time control for bilateral teleoperation system with jittering time delays and state constraints. IFAC, 51(32):588-593.

[27]Wang ZW, Chen Z, Zhang YM, et al., 2019a. Adaptive finite-time control for bilateral teleoperation systems with jittering time delays. Int J Robust Nonl Contr, 29(4):1007-1030.

[28]Wang ZW, Chen Z, Liang B, 2019b. Fixed-time velocity reconstruction scheme for space teleoperation systems: exp barrier Lyapunov function approach. Acta Astron, 157:92-101.

[29]Xiao B, Yin S, 2016. Velocity-free fault-tolerant and uncertainty attenuation control for a class of nonlinear systems. IEEE Trans Ind Electr, 63(7):4400-4411.

[30]Xue T, Wang Z, Zhang T, et al., 2018. The control system for flexible hip assistive exoskeleton. Proc IEEE Int Conf on Robotics and Biomimetics, p.697-702.

[31]Xue T, Wang Z, Zhang T, et al., 2019. Adaptive oscillator-based robust control for flexible hip assistive exoskeleton. IEEE Robot Autom Lett, 4(4):3318-3323.

[32]Yang Y, Hua CC, Li JP, et al., 2017. Finite-time output-feedback synchronization control for bilateral teleoperation system via neural networks. Inform Sci, 406:216-233.

[33]Yang ZY, Gu WJ, Zhang J, et al., 2017. Force Control Theory and Method of Human Load Carrying Exoskeleton Suit. Springer, Berlin, Germany.

[34]Zhang T, Tran M, Huang H, 2019. Admittance shaping-based assistive control of SEA-driven robotic hip exoskeleton. IEEE/ASME Trans Mech, 24(4):1508-1519.

[35]Zhu Z, Xia Y, Fu M, 2011. Attitude stabilization of rigid spacecraft with finite-time convergence. Int J Robust Nonl Contr, 21(6):686-702.

[36]Zoss AB, Kazerooni H, Chu A, 2006. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Trans Mech, 11(2):128-138.