Efficient Estimation of Relaxed Model Parameters for Robust UAV Trajectory Optimization

TL;DR

This paper introduces a computationally efficient affine-in-parameters model and estimator for UAV trajectory optimization, significantly reducing computation time and improving control robustness under model uncertainties.

Contribution

It proposes a relaxed affine-in-parameters multirotor model and an efficient linear-quadratic estimator for real-time adaptive UAV control.

Findings

98.2% reduction in average solve time

23.9-56.2% decrease in trajectory optimality cost

Improved robustness to model mismatch in simulations

Abstract

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.