Adaptive neural impedance control for electrically driven robotic systems based on a neuro-adaptive observer
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Publication:2022822
DOI10.1007/S11071-020-05569-8zbMath1459.93083OpenAlexW3011049721MaRDI QIDQ2022822
Jinzhu Peng, Zeqi Yang, Shuai Ding, Jianbin Xin
Publication date: 30 April 2021
Published in: Nonlinear Dynamics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11071-020-05569-8
neural networksstate observerimpedance controlback-stepping techniqueelectrically driven robotic manipulator
Artificial neural networks and deep learning (68T07) Adaptive control/observation systems (93C40) Automated systems (robots, etc.) in control theory (93C85)
Related Items (5)
Adaptive finite-time impedance backstepping control for uncertain robotic systems interacting with unknown environments ⋮ Practical tracking control under actuator saturation for a class of flexible-joint robotic manipulators driven by DC motors ⋮ Adaptive finite-time optimised impedance control for robotic manipulators with state constraints ⋮ Brenke-type polynomials-based robust adaptive control of cooperative multiple manipulators without velocity measurements ⋮ A sliding mode observer for robust fault reconstruction in a class of nonlinear non-infinitely observable descriptor systems
Cites Work
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- Adaptive neural network-based saturated control of robotic exoskeletons
- Position/force tracking impedance control for robotic systems with uncertainties based on adaptive Jacobian and neural network
- Impedance control of robots using voltage control strategy
- Robust tracking control of rigid-link electrically-driven robots
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