Search

This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the predictive control design with an accurate dynamic model by means of a hybrid modeling technique. For this purpose, an indigenously developed ducted fan miniature aerial vehicle with adequate flying capabilities is used. The physics-informed dynamical model is derived offline by considering the forces and moments acting on the platform. On the basis of the physics-informed model, a data-driven ML approach called adaptive sparse identification of nonlinear dynamics is utilized for model identification, estimation, and correction online. Thereafter, an MPC-based optimization problem is computed by updating the physics-informed states with the physics-informed ML model at each step, yielding an effective control performance. Closed-loop stability and recursive feasibility are ensured under sufficient conditions. Finally, a simulation study is conducted to concisely corroborate the efficacy of the presented framework.

In this paper, we focus on the hover/high-speed-cruising transition flight control of a ducted fan unmanned aerial vehicle (UAV), in which saturation on control surfaces is taken into consideration. To achieve full-envelope flight control, we propose an adaptive cascaded structure on the velocity tracking control for the nonlinear aircraft system. By introducing adaptive neural network, the closed-loop system tracks a prescribed reference system with all the nonlinear tracking errors well canceled. In the meanwhile, to tackle the control surface saturation, we propose a control effectiveness enhancement (CEE) algorithm in attaining sufficient control moment to the ducted fan vehicle. Under the proposed cascaded adaptive control scheme in conjunction with the CEE algorithm, the state tracking performance is proved to be bounded when encountering surface saturation. Finally, comparison experiments in terms of the forward and backward flight transition are conducted on the ducted fan UAV. The results are satisfactory and convincing in exhibiting the flight transition course that demonstrates the effectiveness of the proposed method.

In this paper, we address the hover-to-cruise transition control problem for high-speed level flight of a ducted fan UAV. Firstly, the nonlinear aircraft dynamic system is derived and relevant key aerodynamic characteristics are presented. Then, we divide the dynamic system into known parts and uncertain parts. In order to track a reference system formed by the known parts, we propose a neural-network-based control strategy to learn the error dynamics and cancel the tracking error with proved stability. Finally, simulations and experiments are conducted to perform the transition process at fixed altitude and constant horizontal acceleration, with satisfactory results accomplishing the designed flight course.