@article{Yang2026, 
author = {Hao Yang and Haixiang Jiang and Hailong Pei and Xinpan Gou},
title = {Control Approach for Disturbed Autonomous Helicopters Preserving Nonaffine and Non-Minimum Phase Characters},
year = {2026},
journal = {Unmanned Systems},
volume = {14},
number = {1},
pages = {215-226},
keywords = {Autonomous unmanned helicopter, nonaffine, non-minimum phase, singular perturbation theory, disturbance-rejection, nonlinear dynamic inversion},
url = {https://www.sciopen.com/article/10.1142/S2301385025500906},
doi = {10.1142/S2301385025500906},
abstract = {This paper addresses the importance of accounting for both nonaffine-in-control structure and non-minimum phase behavior in the control of autonomous unmanned helicopters (AUHs). Many existing control methods for AUHs tend to simplify these complexities. Such approximations can restrict operational regime, compromise flight performance, and potentially lead to system instability. To overcome these limitations, we introduce a feasible control strategy for 3-DOF AUHs that preserves these essential characteristics while also accounting for external disturbances. First, we construct an augmented fast subsystem to transform the closed-loop system into the standard singular perturbed form. Thereafter, we derive and manipulate the order-reduced subsystems separately in the fast and slow time-scales. In the fast time-scale, the flapping angle is exponentially stabilized around the desired manifold. In the slow time-scale, we transform AUHs into a simpler affine form compared to the original model through the manifold derived in the fast time-scale, and then an observer-based nonlinear controller is designed to guarantee the uniformly ultimately bounded stability of the resulting order-reduced slow subsystem. Finally, the tracking performance of the entire system is analyzed and ensured under the singular perturbation theory. To illustrate the effectiveness of our method, we present two simulation examples comparing our approach with existing methods using simplified models. The analysis of the results highlights the issues arising from these approximations, and demonstrates the superior performance of our proposed control method in terms of reduced amplitude of input, decreased setting time, and improved tracking accuracy.}
}