通过简化模型提升无人机轨迹优化的实时性与鲁棒性
Efficient Estimation of Relaxed Model Parameters for Robust UAV Trajectory Optimization
- 采用参数线性化的飞行器模型，将非线性估计转为快速求解的二次规划
- 相比传统方法，求解时间减少98.2%，轨迹优化成本降低23.9%至56.2%
- 适合资源受限的无人机平台，用于农业、巡检等需自适应控制场景
在线轨迹优化与最优控制对实现可持续无人机服务（如农业、环境监测、运输）至关重要，但其对模型失配敏感，常见于负载变化或推力参数波动。为缓解此问题，可结合参数估计算法实现自适应控制，但无人机算力有限，非线性参数估计常因计算开销过大难以部署。本文提出一种仿射-参数形式的多旋翼松弛模型，将原始移动窗口参数估计（MHPE）问题转化为线性-二次形式（LQ-MHPE），使求解变为快速二次规划（QP），支持实时模型预测控制（MPC）。在蒙特卡洛仿真中，该方法相较原非线性估计算法，平均求解时间下降98.2%，轨迹最优性成本降低23.9%-56.2%。
原文摘要 · Abstract (English)
Online trajectory optimization and optimal control methods are crucial for enabling sustainable unmanned aerial vehicle (UAV) services, such as agriculture, environmental monitoring, and transportation, where available actuation and energy are limited. However, optimal controllers are highly sensitive to model mismatch, which can occur due to loaded equipment, packages to be delivered, or pre-existing variability in fundamental structural and thrust-related parameters. To circumvent this problem, optimal controllers can be paired with parameter estimators to improve their trajectory planning performance and perform adaptive control. However, UAV platforms are limited in terms of onboard processing power, oftentimes making nonlinear parameter estimation too computationally expensive to consider. To address these issues, we propose a relaxed, affine-in-parameters multirotor model along with an efficient optimal parameter estimator. We convexify the nominal Moving Horizon Parameter Estimation (MHPE) problem into a linear-quadratic form (LQ-MHPE) via an affine-in-parameter relaxation on the nonlinear dynamics, resulting in fast quadratic programs (QPs) that facilitate adaptive Model Predictve Control (MPC) in real time. We compare this approach to the equivalent nonlinear estimator in Monte Carlo simulations, demonstrating a decrease in average solve time and trajectory optimality cost by 98.2% and 23.9-56.2%, respectively.
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