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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| Published in | Nonlinear dynamics Vol. 100; no. 2; pp. 1359 - 1378 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.04.2020
Springer Nature B.V |
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| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Peng, Jinzhu Ding, Shuai Xin, Jianbin Yang, Zeqi |
| Author_xml | – sequence: 1 givenname: Jinzhu orcidid: 0000-0002-2823-6571 surname: Peng fullname: Peng, Jinzhu organization: School of Electrical Engineering, Zhengzhou University – sequence: 2 givenname: Shuai surname: Ding fullname: Ding, Shuai organization: School of Electrical Engineering, Zhengzhou University – sequence: 3 givenname: Zeqi surname: Yang fullname: Yang, Zeqi organization: School of Electrical Engineering, Zhengzhou University – sequence: 4 givenname: Jianbin surname: Xin fullname: Xin, Jianbin email: j.xin@zzu.edu.cn organization: School of Electrical Engineering, Zhengzhou University |
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| CitedBy_id | crossref_primary_10_1016_j_neucom_2021_05_095 crossref_primary_10_1080_00207179_2022_2146604 crossref_primary_10_1007_s11063_022_10827_4 crossref_primary_10_1007_s11071_023_09008_2 crossref_primary_10_1007_s11071_020_05843_9 crossref_primary_10_1007_s11071_022_08112_z crossref_primary_10_3233_JIFS_224250 crossref_primary_10_1177_00202940221090970 crossref_primary_10_1631_FITEE_2000145 crossref_primary_10_1109_TSMC_2023_3324761 crossref_primary_10_1007_s00170_021_08417_0 crossref_primary_10_1109_ACCESS_2024_3383782 crossref_primary_10_1080_00207721_2023_2213230 crossref_primary_10_3390_act12080305 crossref_primary_10_1007_s11370_024_00522_9 crossref_primary_10_1080_00207179_2022_2111365 crossref_primary_10_1016_j_asoc_2021_108142 crossref_primary_10_1002_rnc_7397 crossref_primary_10_1007_s11071_022_07602_4 |
| Cites_doi | 10.1016/j.camwa.2012.03.020 10.1080/00207729408928986 10.1016/j.neucom.2007.02.012 10.1109/TNNLS.2018.2803827 10.1007/s11071-017-3454-9 10.1152/jn.2001.86.2.1047 10.1016/j.neucom.2018.11.068 10.1016/j.eswa.2018.11.040 10.1007/s11071-019-05073-8 10.1016/j.isatra.2017.07.029 10.1016/j.isatra.2019.02.009 10.1007/s11071-018-4348-1 10.1109/70.68075 10.1109/TIE.2014.2352605 10.1007/s11071-013-0964-y 10.1007/s11071-012-0546-4 10.1109/ACCESS.2016.2618373 10.1017/S0263574711000403 10.1007/s11071-018-4308-9 10.3103/S0146411616050047 10.1007/s11071-014-1477-z 10.1007/s11071-016-2720-6 10.1016/j.procs.2017.12.009 10.1109/TNNLS.2017.2665581 10.1016/j.neucom.2017.05.011 10.1016/j.ymssp.2018.03.042 10.1109/ICInfA.2016.7832083 10.1109/IROS.2009.5354136 10.1109/HSI.2017.8005020 |
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| Keywords | Back-stepping technique State observer Neural networks Impedance control Electrically driven robotic manipulator |
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| SubjectTerms | 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 |
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| Title | Adaptive neural impedance control for electrically driven robotic systems based on a neuro-adaptive observer |
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