Adaptive Neural Dynamic Surface Control Of A Stratospheric Airship With Time-Varying Full State Constraints And Disturbances

This paper investigates the attitude tracking control of a stratospheric airship with unmodeled dynamics, external disturbances and full state constraints. The dynamic surface control is established to reduce the complex computational caused by the differential of virtual control laws. A new adaptive law of radial basis function neural networks (RBFNNs) is proposed to approximate the unmodeled dynamics. The nonlinear disturbance observer (NDOB) is designed to estimate unknown external disturbances. The asymmetric time-varying barrier Lyapunov function (ATBLF) is established to prevent the asymmetric t ime-varyingfull state constraints are overstepped. The simulat ion for stratospheric airship are given to verify the effectiveness of the developed control scheme.