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Adaptive Sliding Mode Based Disturbance Attenuation Tracking Control for Wheeled Mobile Robots

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Abstract

This paper is devoted to investigating a composite controller for wheeled mobile robots in the presence of external disturbance and parametric uncertainty. Unlike the traditional backstepping technique existing the impractical velocity jumps, the proposed neural dynamic model has the ability to generate smooth continuous signals. Subsequently, a disturbance observer based adaptive sliding mode dynamic controller is introduced to estimate disturbances online, adjust control gain automatically and eliminate chattering phenomena completely. Under the developed control law, the ultimate boundedness of all signals is guaranteed and the tracking errors can be arbitrarily small in finite time. Simulation results are carried out to demonstrate the effectiveness of the proposed scheme.

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Abbreviations

DO:

Disturbance observer

WMR:

Wheeled mobile robot

NDMCBC:

Neural dynamic model based classic nackstepping control

ASMC:

Adaptive sliding mode control

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Correspondence to Hongbo Gao.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Quoc Chi Nguyen under the direction of Editor Myo Taeg Lim. This work was supported by the National Key Research and Development Program of China under Grant No. 2017YFB0102603, grants from NVIDIA and the NVIDIA DGX-Station/DRIVE PX 2 AutoChauffeur, the National Basic Research Program of China (973 Program: 2013CB73310)and the National Natural Science Foundation of China under Grant 61273090.

Kang Liu received his B.S. degree in Department of Automation from Donghua University, Shanghai, China, in 2017. He is currently working toward a Ph.D. degree in Department of Automation, University of Science and Technology of China, Hefei, China. His current research interests include intelligent driving, artificial intelligence, and adaptive control.

Hongbo Gao received his Ph.D. degree from Beihang University, Beijing, China, in 2016. He is currently an associate professor with the Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Anhui Province, China, He is the author or coauthor of over 30 journal papers, and he is the co-holder of 6 patent applications. His current research interests include unmanned system platform and robotics, machine learning, decision support system, intelligent driving.

Haibo Ji received his B.Eng. and Ph.D. degrees in Mechanical Engineering from Zhejiang University and Beijing University, in 1984 and 1990, respectively. He is currently a professor in Department of Automation, University of Science and Technology of China, Hefei, China. His research interests include nonlinear control and adaptive control.

Zhengyuan Hao received his B.E. degree in Department of Automation from Shandong University in 2019, Jinan China. He is currently working toward an M.S. degree in Department of Automation, University of Science and Technology of China, Hefei, China. His current research interests include pattern recognition and intelligent vehicles.

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Liu, K., Gao, H., Ji, H. et al. Adaptive Sliding Mode Based Disturbance Attenuation Tracking Control for Wheeled Mobile Robots. Int. J. Control Autom. Syst. 18, 1288–1298 (2020). https://doi.org/10.1007/s12555-019-0262-7

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