article · 2024

Morphing UAV Formation Control Based on Adaptive Dynamic Programming

Ruifeng Li, Liqian Dou, Hui Yu, Zheyu Zhang, Hao Cheng

Cited by 0Abstract and details only

Abstract

In this study, an adaptive dynamic programming-based collaborative tracking control algorithm is developed for the formation of morphing unmanned aerial vehicles (UAVs). The multi-morphing UAVs system is divided into the position and attitude subsystems. For the position subsystem, a novel cost function fused with the impact of UAV morphing is introduced and the position controllers for morphing UAVs are developed using adaptive dynamic programming (ADP). To quantify the impact of morphing, a high-order finite-time observer is introduced which estimates the acceleration from aerodynamic force acting on the wings. For the attitude subsystem, an integral back-stepping controller is constructed. The stability of morphing UAV formation is theoretically proven with the Lyapunov function. Simulations involving comparisons with PID algorithm and sliding mode control (SMC) method have been conducted to validate its effectiveness and robustness.

Cite this paper

APA 7

Li, R., Dou, L., Yu, H., Zhang, Z., & Cheng, H. (2024). Morphing UAV Formation Control Based on Adaptive Dynamic Programming. https://doi.org/10.23919/ccc63176.2024.10662690

MLA 9

Li, Ruifeng, et al. “Morphing UAV Formation Control Based on Adaptive Dynamic Programming.” 2024, pp. 3876–81, https://doi.org/10.23919/ccc63176.2024.10662690.

Chicago

Li, Ruifeng, Liqian Dou, Hui Yu, Zheyu Zhang, and Hao Cheng. 2024. “Morphing UAV Formation Control Based on Adaptive Dynamic Programming.” https://doi.org/10.23919/ccc63176.2024.10662690.

Harvard

Li, R. et al. (2024) ‘Morphing UAV Formation Control Based on Adaptive Dynamic Programming’. Available at: https://doi.org/10.23919/ccc63176.2024.10662690.

BibTeX

@article{li2024morphing,
  title = {{Morphing UAV Formation Control Based on Adaptive Dynamic Programming}},
  author = {Li, Ruifeng and Dou, Liqian and Yu, Hui and Zhang, Zheyu and Cheng, Hao},
  pages = {3876--3881},
  year = {2024},
  doi = {10.23919/ccc63176.2024.10662690}
}

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