An adaptive neural sliding mode control with ESO for uncertain nonlinear systems is proposed to improve the stability of the control system. Any control system inevitably exists uncertain disturbances and nonlinearities which severely affect the control performance and stability. Neural network can be utilized to approximate the uncertain nonlinearities. Nevertheless, it produces approximate errors, which will become more difficult to deal with as the order of the system increases. Moreover, these errors and uncertain disturbances will result in a consequence that the control system can be unable to converge quickly, and has to deal with a lot of calculations. Therefore, in order to perfect the performance and stability of the control system, this paper combines sliding mode control and ESO, and designs an adaptive neural control method. The simulation results illustrate that the improved system has superior tracking performance and anti-interference ability.
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Recommended by Associate Editor Yajuan Liu under the direction of Editor Jessie (Ju H.) Park. This work is supported by the National Natural Science Foundation (NNSF) of China (Grant Numbers 51775122 and 51505092), and the Science and Technology Planning Project of Guangdong (Grant Number 2016B090912007), and the Program of Foshan Innovation Team of Science and Technology (Grant Number 2015IT100072), and Natural science foundation of guangdong province (Grant Number 2019A1515110995), and the Innovative Talents Project of Guangdong Eduction Department (Grant Number 2018KQNCX197), and the Science and Technology Planning Project of Guangzhou (Grant Number 202002030286). The authors would like to express our sincere appreciations to Editor, Associate Editor and referees for their valuable comments and suggestions, which have helped to improve the quality of this paper greatly. The authors declare that there are no conflicts of interest regarding the publication of this paper.
Jianhui Wang received his Ph.D. and M.S. degrees from the School of Automation, Guangdong University of Technology, Guangzhou, China, in 2019 and 2009, respectively. Since 2009, he has been with the School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou. His current research interests include nonlinear systems, linear systems, and intelligent control.
Peisen Zhu as a senior student, studies in the School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, China. His current research interests include nonlinear systems, and intelligent control.
Biaotao He will graduate from the School Mechanical and Electric Engineering, Guangzhou University, Guangdong, China in 2020. His main research interests include nonlinear control and fuzzy logic system control.
Guiyang Deng received his Ph.D. degree in control science and engineering from he Faculty of Automation, Guangdong University of Technology, China in 2019. His current research interest is about Electric automatization, Artificial intelligence and Robot.
Chunliang Zhang received his Ph.D. degree from the School of Zhejiang University, Zhejiang, China, in 2004. Since 2008, he has been with the School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou. He is now the dean of the college. His current research interests include Vibration control, and intelligent control.
Xing Huang is currently pursuing a B.S. degree with the School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, China. Since 2016, he has been with the School of Mechanical and Electric Engineering, Guangzhou University. His current research interests include nonlinear systems and linear systems.
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Wang, J., Zhu, P., He, B. et al. An Adaptive Neural Sliding Mode Control with ESO for Uncertain Nonlinear Systems. Int. J. Control Autom. Syst. 19, 687–697 (2021). https://doi.org/10.1007/s12555-019-0972-x