🤖 AI Summary
To address trajectory optimization instability, high energy consumption, and poor real-time performance of resource-constrained UAVs under model mismatch (e.g., payload or structural changes), this paper proposes a synergistic framework integrating affine-parameterized multirotor dynamics modeling and online adaptive control. The nonlinear dynamics are innovatively relaxed into an affine parameter form, enabling formulation of a convexifiable moving-horizon parameter estimation (MHPE) problem, which is equivalently transformed into a linear-quadratic MHPE (LQ-MHPE) formulation. This allows tight closed-loop integration with model predictive control (MPC). Compared to nonlinear estimation methods, the proposed approach reduces average computation time by 98.2% and lowers trajectory optimization cost by 23.9%–56.2%. It is the first method to enable onboard real-time adaptive MPC for multirotors, achieving high accuracy, low computational complexity, and strong robustness against model uncertainties.
📝 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.