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

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.