Open AccessA neural network combined with sliding mode controller for the two-wheel self-balancing robot
Author(s) -
Duc-Minh Nguyen,
Van-Tiem Nguyen,
Trong-Thang Nguyen
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i3.pp592-601
Subject(s) - computer science , control theory (sociology) , controller (irrigation) , artificial neural network , sliding mode control , lyapunov stability , correctness , robot , noise (video) , compensation (psychology) , lyapunov function , control (management) , artificial intelligence , nonlinear system , algorithm , psychology , physics , quantum mechanics , psychoanalysis , agronomy , image (mathematics) , biology
This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.