Modified adaptive neural dynamic surface control for morphing aircraft with input and output constraints

In this paper, a barrier Lyapunov function-based adaptive neural dynamic surface control approach is proposed for morphing aircraft subject to unknown parameters and input–output constraints. Based on the functional decomposition, the longitudinal dynamics can be divided into altitude and velocity subsystems. Minimal learning parameter (MLP) technique-based neural networks are used to estimate the model uncertainties; thus, the amount of online-updated parameters is largely reduced. To overcome the problem of ‘explosion of complexity’ in the back-stepping method, the first-order sliding mode differentiator (FOSD) is introduced to compute the derivative of virtual control laws. Combining MLP and FOSD technique, a composite adaptive neural control scheme is proposed by utilizing an auxiliary system to deal with the input saturation and a barrier Lyapunov function to counteract the output constraints. The highlight is that the proposed neural controller not only owns less online-updated neural parameters, but also has the ability of handling input–output constraints. The stability of the proposed control scheme is established using the Lyapunov theory. Simulation results show that the proposed controller can ensure good tracing performance of the morphing aircraft in the fixed configuration and morphing process.