scholarly journals Active disturbance rejection control of a chaotic system based on immune binary-state particle swarm optimization algorithm

2011 ◽  
Vol 60 (1) ◽  
pp. 019501
Author(s):  
Wu Jian-Hui ◽  
Zhang Jing ◽  
Zhang Ying-Jie ◽  
Liu Zhao-Hua
Keyword(s):  
Particle Swarm Optimization ◽  
Chaotic System ◽  
Optimization Algorithm ◽  
Disturbance Rejection ◽  
Particle Swarm Optimization Algorithm ◽  
Active Disturbance Rejection Control ◽  
Swarm Optimization ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Binary State

scholarly journals Quadrotor Attitude Control by Fractional-Order Fuzzy Particle Swarm Optimization-Based Active Disturbance Rejection Control

2021 ◽  
Vol 11 (24) ◽  
pp. 11583
Author(s):  
Qi Zhang ◽  
Yaoxing Wei ◽  
Xiao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Attitude Control ◽  
Disturbance Rejection ◽  
Particle Swarm ◽  
Active Disturbance Rejection Control ◽  
Swarm Optimization ◽  
Quadrotor Aircraft ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of ADRC, a fractional-order fuzzy particle swarm optimization (FOFPSO) algorithm is proposed to optimize the parameters of ADRC for quadrotor aircraft. Simultaneously, the simulation experiment is designed, which compares with the optimized performance of traditional particle swarm optimization (PSO), fuzzy article swarm optimization (FPSO) and adaptive genetic algorithm-particle swarm optimization (AGA-PSO). In addition, the turbulent wind field model is established to verify the disturbance rejection performance of the controller. Finally, the designed controller is deployed to the actual hardware platform by using the model-based design method. The results show that the controller has a small overshoot and stronger disturbance rejection ability after the parameters are optimized by the proposed algorithm.

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