Adaptive learning finite‐time switching control of a helicopter system with state constraints

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.