Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators

Xi，Rui Dong1,2; Ma，Tie Nan1; Xiao，Xiao2,3; Yang，Zhi Xin1

2023

10.1177/01423312231190169

Transactions of the Institute of Measurement and Control   影响因子和分区

0142-3312

1477-0369

Robot manipulators as an indispensable part of automatic operation are becoming increasingly important in intelligent manufacturing systems, such as grinding and assembly. Although control methods of robot manipulators have been extensively studied, high-precision robots still face new challenges from uncertainties caused by changes in the environment and enhancement of interference. As a consequence, the neural network-based observer is utilized to reduce the influence of uncertainties and external disturbances. In this work, a new kind of nonlinear disturbance observer is designed which could well function with observed states. To improve the control efficiency and response characteristic, a kind of new integral sliding manifold is devised and the control input is generated via backstepping techniques. The stability is proved with Lyapunov theory, and the experimental results are given to demonstrate the effectiveness of the proposed controller.

disturbance observer RBF neural networks Robot control sliding mode control (SMC) state observer

SCI ; EI

英语

其他

Science and Technology Development Fund, Macau SAR["0018/2019/AKP","SKL-IOTSC(UM)-2021-2023"] ; Guangdong Science and Technology Department[2020B1515130001] ; University of Macau[MYRG2022-00059-FST] ; Zhuhai UM Research Institute[HF-011-2021]

Automation & Control Systems ; Instruments & Instrumentation

Automation & Control Systems ; Instruments & Instrumentation

WOS:001044677700001

SAGE PUBLICATIONS LTD

20233314531738

Adaptive control systems ; Controllers ; Flexible manipulators ; Grinding (machining) ; Industrial robots ; Intelligent robots ; Machine design ; Modular robots ; Robot applications ; Sliding mode control

Mechanical Design:601 ; Machining Operations:604.2 ; Control Systems:731.1 ; Robotics:731.5 ; Robot Applications:731.6 ; Control Equipment:732.1 ; Mathematics:921 ; Systems Science:961

ENGINEERING

2-s2.0-85167453302

Scopus

1.State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering,University of Macau,China
2.Department of Electronic and Electrical Engineering,Southern University of Science and Technology,China
3.Yuanhua Robotics,Perception & AI Technologies Ltd,China

Xi，Rui Dong,Ma，Tie Nan,Xiao，Xiao,等. Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators[J]. Transactions of the Institute of Measurement and Control,2023.

Xi，Rui Dong,Ma，Tie Nan,Xiao，Xiao,&Yang，Zhi Xin.(2023).Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators.Transactions of the Institute of Measurement and Control.

Xi，Rui Dong,et al."Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators".Transactions of the Institute of Measurement and Control (2023).