Adaptive learning finite‐time switching control of a helicopter system with state constraints
Year: 2026
Type: article
Abstract: This paper addresses the trajectory tracking problem of the 2‐DOF helicopter system by proposing an adaptive finite‐time control method based on a composite learning and switching mechanism. Unlike traditional learning control approaches, the composite learning method in this paper improves learning efficiency by introducing a prediction error term within the neural domain. Outside the neural domain, the switching mechanism ensures rapid convergence of the system state. Additionally, a barrier Lyapunov function (BLF) is employed to impose state constraints, ensuring that the system state remains within a safe range during the learning process. Through Lyapunov stability analysis, the finite‐time convergence and uniform ultimate boundedness of the system are proven. Finally, simulations and experiments validate that the proposed method effectively overcomes the challenges posed by model uncertainties and external disturbances.
Source: Asian Journal of Control
DOI: 10.1002/asjc.70072
Authors: Xian Luo, Yusheng Lin
Institution: Guangzhou Railway Polytechnic
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7128427194