Affiliations: [a] College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan, Guangdong, China | [b] School of Electro-mechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China
Correspondence:
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Corresponding author: Jianhao Nie, College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan, Guangdong 523000, China. E-mail: [email protected].
Abstract: Aiming at improving the response speed and robustness of wheeled mobile robots, this paper uses neural networks to identify the dynamic functions of mobile robots, and proposes an improved adaptive super-twisting sliding mode controller. First, this paper improves the sliding mode surface of super-twisting sliding mode control, which effectively speeds up the response speed of the system. Second, the robust adaptive law is utilized to eliminate the influence of uncertain parameters in super-twisting sliding mode control, which improves the robustness of the system and greatness reduces the chattering. In addition, the use of a high-gain observer to estimate the speed information of the mobile robot in real time avoids the shortcomings of direct measurement of speed information and realizes the output feedback control of the system.
