Abstract
This paper investigates the finite-time attitude tracking control problem of an autonomous airship with uncertainties and full state constraints. An adaptive finite-time neural backstepping control approach is designed by using integral barrier Lyapunov functionals. Radial basis function neural networks are applied to model the uncertainties. A finite-time convergence differentiator is introduced to estimate the time derivative of virtual control law. The stability analysis shows that all the closed-loop signals of airship system are bounded, the state constraints are not violated, and the convergence of attitude tracking error in small neighborhood of the origin in a finite time can be guaranteed. Simulations are performed to verify the effectiveness of the control approach.
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