Open AccessOptimized neural network based sliding mode control for quadrotors with disturbances
Author(s) -
Ping Li,
Zhe Lin,
Hong Lei Shen,
Zhaoqi Zhang,
Mei Xiao-hua
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021092
Subject(s) - control theory (sociology) , particle swarm optimization , convergence (economics) , artificial neural network , computer science , trajectory , position (finance) , sliding mode control , tracking (education) , mode (computer interface) , control (management) , artificial intelligence , algorithm , nonlinear system , physics , psychology , pedagogy , finance , astronomy , quantum mechanics , economics , economic growth , operating system
In this paper, optimized radial basis function neural networks (RBFNNs) are employed to construct a sliding mode control (SMC) strategy for quadrotors with unknown disturbances. At first, the dynamics model of the controlled quadrotor is built, where some unknown external disturbances are considered explicitly. Then SMC is carried out for the position and the attitude control of the quadrotor. However, there are unknown disturbances in the obtained controllers, so RBFNNs are employed to approximate the unknown parts of the controllers. Furtherly, Particle Swarm optimization algorithm (PSO) based on minimizing the absolute approximation errors is used to improve the performance of the controllers. Besides, the convergence of the state tracking errors of the quadrotor is proved. In order to exposit the superiority of the proposed control strategy, some comparisons are made between the RBFNN based SMC with and without PSO. The results show that the strategy with PSO achieves quicker and smoother trajectory tracking, which verifies the effectiveness of the proposed control strategy.