构建可复现的四旋翼控制评估框架，支持多种干扰下的性能测试。
A Simulation Evaluation Suite for Robust Adaptive Quadcopter Control
- 基于RotorPy搭建模块化仿真平台，集成多种干扰模型。
- 支持风扰、负载偏移等6类干扰，验证控制精度与鲁棒性。
- 提供统一测试环境，适合算法对比与自动化压力测试。
鲁棒自适应控制对四旋翼在外部干扰和模型不确定性下的性能至关重要。然而，任务、模拟器和实现方式的碎片化评估阻碍了方法间的系统比较。本文提出一个基于RotorPy的轻量级、模块化四旋翼控制仿真测试平台，支持风扰、载荷偏移、电机故障和控制延迟等多种干扰场景。框架内置典型自适应与非自适应控制器库，并提供任务相关的跟踪精度与鲁棒性评估指标。统一的模块化环境实现跨方法可复现评估，避免重复实现干扰模型、轨迹生成器和分析工具。通过多类干扰与轨迹示例，展示其在系统性分析中的通用性，包括自动化压力测试。代码已开源：https://github.com/Dz298/AdaptiveQuadBench。
原文摘要 · Abstract (English)
Robust adaptive control methods are essential for maintaining quadcopter performance under external disturbances and model uncertainties. However, fragmented evaluations across tasks, simulators, and implementations hinder systematic comparison of these methods. This paper introduces an easy-to-deploy, modular simulation testbed for quadcopter control, built on RotorPy, that enables evaluation under a wide range of disturbances such as wind, payload shifts, rotor faults, and control latency. The framework includes a library of representative adaptive and non-adaptive controllers and provides task-relevant metrics to assess tracking accuracy and robustness. The unified modular environment enables reproducible evaluation across control methods and eliminates redundant reimplementation of components such as disturbance models, trajectory generators, and analysis tools. We illustrate the testbed's versatility through examples spanning multiple disturbance scenarios and trajectory types, including automated stress testing, to demonstrate its utility for systematic analysis. Code is available at https://github.com/Dz298/AdaptiveQuadBench.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。