Abstract
In this paper, the tracking control problem of flexible joint robotic manipulator (FJRM) system subjected to system uncertainties and time-varying external disturbances (TVED) is addressed. A new disturbance observer-based neural network integral sliding mode controller with output constraints (DNISMCOC) that comprises the merits of neural networks, disturbance observer and integral sliding mode is proposed. Considering that the radial basis function neural network (RBFNN) has a fast learning convergence speed and great approximation ability, two matrices of RBFNN are utilized to estimate the parameter matrices of the dynamic model of FJRM. In view of the estimation errors of RBFNNs and TVED in the system, a disturbance observer is introduced to estimate the lump uncertainties which consist of them. Integral sliding mode is introduced for eliminating steady errors further. For ensuring security in some high-accuracy using occasions, a barrier lyapunov functions (BLF) is adopted to achieve output constraints of FJRM. To validate the effectiveness of the proposed control scheme, numerical simulations on 2-link FJRM are conducted. According to the comparisons among DNISMCOC and other state-of-the-art controllers, the superiorities of DNISMCOC in several aspects are proved.
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Quanwei Wen received his B.E. degree in electrical engineering and its automation from Dalian Jiaotong University, Dalian, China, in 2015. Now, he is pursuing an M.E. degree in electric engineering in Nanchang University. His current research interests include sliding mode control, neural network control, and backstepping control of robotic manipulator with flexible joint.
Xiaohui Yang received his B.Sc., M.Sc., and Ph.D. degrees from Nanchang University, Nanchang, China, in 2003, 2006, and 2015, respectively. He has been with the Department of Electronic Information Engineering, School of Information Engineering, Nanchang University, since 2006, where he is currently an Associate Professor. His current research interests include intelligent control and stochastic non-linear systems.
Chao Huang received her B.E. degree in electrical engineering and its automation from Nanchang Institute of Technology, Nanchang, China, in 2020. Now, she is pursuing an M.E. degree in electric engineering in Nanchang University. Her current research interests include sliding mode control, neural network control, adaptive non-linear control of robotic manipulator, and control of circuit-breaker phase selection closing.
Junping Zeng received her B.E. degree in electrical engineering and its automation from Minnan Normal University, China, in 2020. Now, she is pursuing an M.E. degree in electric engineering in Nanchang University. Her current research interests include sliding mode control and combined CCHP microgrids with integrated demand response.
Zhixin Yuan received his B.E. degree in electrical engineering and its automation from East China Jiaotong University, Nanchang, China, in 2020. Now, he is pursuing an M.E. degree in electric engineering in Nanchang University. He current research interests include optimal configuration of microgrid and grid-connected technology of microgrid.
Peter Xiaoping Liu received his B.Sc. and M.Sc. degrees from Northern Jiaotong University, China in 1992 and 1995, respectively, and a Ph.D. degree from the University of Alberta, Canada in 2002. He has been with the Department of Systems and Computer Engineering, Carleton University, Canada since July 2002, and he is currently a Professor and Canada Research Chair. His research interests include interactive networked systems and teleoperation, haptics, micro-manipulation, robotics, and intelligent systems. Dr. Liu is a licensed member of the Professional Engineers of Ontario (P.Eng), a senior member of IEEE and Fellow of Engineering Institute of Canada (FEIC).
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This work is supported by the National Natural Science Foundation of China (51765042, 61963062).
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Wen, Q., Yang, X., Huang, C. et al. Disturbance Observer-based Neural Network Integral Sliding Mode Control for a Constrained Flexible Joint Robotic Manipulator. Int. J. Control Autom. Syst. 21, 1243–1257 (2023). https://doi.org/10.1007/s12555-021-0972-5
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DOI: https://doi.org/10.1007/s12555-021-0972-5