Department of Automatic Control, Northwestern Polytechnical University, China.
Department of Automatic Control, Northwestern Polytechnical University, China.
ISA Trans. 2023 Apr;135:35-51. doi: 10.1016/j.isatra.2022.09.021. Epub 2022 Sep 18.
The stability of formation flight is not only sensitive to external disturbances but also to data observed and transferred between sensors and unmanned aerial vehicles (UAVs). A multi-constrained model predictive control (MPC) strategy, combined with Kalman-consensus filter (KCF) and fixed-time disturbance observer (FTDOB) is developed for the formation control of multiple quadrotors here Firstly, KCF is used to effectively fuse the data shared in the formation with noise and uncertainty, which improves the applicability and robustness of the formation in complex environments. Secondly, FTDOB is able to estimate the external disturbances suffered by the quadrotor in a fixed time and provides real-time compensation for the controller. On this basis, an improved MPC (IMPC) is designed for each UAV of the formation, which improves the computational efficiency while ensuring the asymptotic stability of the system. Eventually, the capability and effectiveness of the proposed strategy are verified by simulation in terms of disturbance rejection and noise suppression, as well as good trajectory tracking of the formation.
编队飞行的稳定性不仅对外部干扰敏感，而且对传感器和无人机 (UAV) 之间观测和传输的数据也敏感。这里提出了一种多约束模型预测控制 (MPC) 策略，结合卡尔曼一致滤波器 (KCF) 和固定时间干扰观测器 (FTDOB)，用于多旋翼的编队控制。首先，KCF 用于有效地融合编队中共享的数据，同时处理噪声和不确定性，从而提高了编队在复杂环境中的适用性和鲁棒性。其次，FTDOB 能够在固定时间内估计四旋翼飞行器所受的外部干扰，并为控制器提供实时补偿。在此基础上，为编队中的每架 UAV 设计了改进的 MPC (IMPC)，在保证系统渐近稳定性的同时提高了计算效率。最终，通过仿真验证了所提出策略在干扰抑制和噪声抑制以及编队的良好轨迹跟踪方面的性能和有效性。
