用机载视觉数据识别四旋翼模型参数，提升控制精度。
Vision-Based System Identification of a Quadrotor
- 结合视觉数据与灰箱建模，减少推力与阻力系数不确定性。
- 基于视觉识别模型设计的LQR控制器性能稳定一致。
- 适合做无人机自主建模与故障检测研究者参考。
本文探讨了视觉系统在四旋翼建模与控制中的应用。通过实验与分析，解决四旋翼建模中推力与阻力系数的复杂性与局限性问题。采用灰箱建模方法降低不确定性，并评估机载视觉系统的有效性。基于视觉系统采集的数据，构建系统识别模型并设计LQR控制器。结果表明，模型间性能一致，验证了基于视觉的系统识别的有效性。研究揭示了视觉技术在提升四旋翼建模与控制性能方面的潜力，为未来四旋翼性能优化、故障检测与决策机制研究提供支持。
原文摘要 · Abstract (English)
This paper explores the application of vision-based system identification techniques in quadrotor modeling and control. Through experiments and analysis, we address the complexities and limitations of quadrotor modeling, particularly in relation to thrust and drag coefficients. Grey-box modeling is employed to mitigate uncertainties, and the effectiveness of an onboard vision system is evaluated. An LQR controller is designed based on a system identification model using data from the onboard vision system. The results demonstrate consistent performance between the models, validating the efficacy of vision based system identification. This study highlights the potential of vision-based techniques in enhancing quadrotor modeling and control, contributing to improved performance and operational capabilities. Our findings provide insights into the usability and consistency of these techniques, paving the way for future research in quadrotor performance enhancement, fault detection, and decision-making processes.
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