Adaptive neural impedance control for electrically driven robotic systems based on a neuro-adaptive observer

This paper proposes an adaptive neural impedance control (ANIC) strategy for electrically driven robotic systems, considering system uncertainties and external disturbances. For the considered robotic system, the joint velocities and armature currents are assumed to be unknown and unmeasured, and an...

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Bibliographic Details
Published inNonlinear dynamics Vol. 100; no. 2; pp. 1359 - 1378
Main Authors Peng, Jinzhu, Ding, Shuai, Yang, Zeqi, Xin, Jianbin
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.04.2020
Springer Nature B.V
Subjects
Adaptive control
Automotive Engineering
Classical Mechanics
Computer simulation
Control
Dynamical Systems
Engineering
Feedback control
Impedance
Mechanical Engineering
Neural networks
Original Paper
Robot arms
Robotics
Vibration
Back-stepping technique
State observer
Neural networks
Impedance control
Electrically driven robotic manipulator
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Summary:This paper proposes an adaptive neural impedance control (ANIC) strategy for electrically driven robotic systems, considering system uncertainties and external disturbances. For the considered robotic system, the joint velocities and armature currents are assumed to be unknown and unmeasured, and an adaptive observer is then designed to estimate its unknown states using a neural network. Based on the observed joint velocities and armature currents, an ANIC scheme is proposed and the performances of the joint positions and force tracking can be improved. We also prove that the control system is stable and all the signals in closed-loop system are bounded. Simulation examples on a two-link electrically driven robotic manipulator are presented to show the effectiveness of the proposed observer-based intelligent impedance control method.
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-020-05569-8