Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control

被引：63
作者：
Cai, Zhihao [1 ]
Lou, Jiang [1 ]
Zhao, Jiang [1 ]
Wu, Kun [2 ]
Liu, Ningjun [1 ]
Wang, Ying Xun [1 ]
机构：
[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[2] Beihang Univ, Flying Coll, Beijing 100191, Peoples R China
来源：
MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2019年 / 128卷
基金：
中国国家自然科学基金;
关键词：
Quadrotor; Trajectory tracking; Active disturbance rejection control; Chaotic grey wolf optimization (CGWO); Virtual target guidance; Obstacle avoidance; VEHICLE; DESIGN; LINEARIZATION; OBSERVER;
D O I：
10.1016/j.ymssp.2019.03.035
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
In this paper, a new active disturbance rejection control (ADRC) scheme based on swarm intelligent method is proposed for quadrotors to achieve trajectory tracking and obstacle avoidance. First, the finite-time convergent extended state observer (FTCESO) is designed to enhance the performance of ADRC controller. Then, the chaotic grey wolf optimization (CGWO) algorithm is developed with chaotic initialization and chaotic search to obtain the optimal parameters of attitude and position controllers. Further, a novel virtual target guidance approach is proposed to achieve obstacle avoidance for quadrotors. Comparative simulations are presented to demonstrate the effectiveness and robustness of the CGWO-based ADRC scheme and the virtual target guidance approach. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码：636 / 654
页数：19
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