A Simulation Evaluation Suite for Robust Adaptive Quadcopter Control

2025-10-03

AI Summary

Existing robust adaptive control methods for quadrotors lack a unified evaluation benchmark, hindering fair cross-task, cross-simulation-platform, and cross-implementation comparisons. To address this, we propose a modular simulation testing framework built upon RotorPy, which integrates canonical disturbance models—including wind gusts, payload variations, motor failures, and control latency—to enable standardized stress testing of diverse adaptive controllers (e.g., MRAC, L₁ adaptive control, neural-network-based adaptive control). The framework features a configurable trajectory generator, a modular disturbance modeling suite, and a multi-dimensional performance assessment toolkit that quantifies tracking accuracy, stability margins, and recovery capability. Experimental validation demonstrates its effectiveness under compound disturbances, significantly improving evaluation reproducibility and comparability. The open-source implementation facilitates rapid deployment and extensibility for novel algorithm development.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Evaluating robust adaptive quadcopter control methods systematically
• Providing modular simulation testbed for diverse disturbance scenarios
• Enabling reproducible comparison across controllers and implementations

Innovation

• Modular simulation testbed for quadcopter control
• Includes library of adaptive and non-adaptive controllers
• Unified environment enables reproducible evaluation across methods