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

• Published in: Nonlinear dynamics Vol. 87; no. 4; pp. 2367 - 2383
• Main Authors: Wu, Zhonghua, Lu, Jingchao, Zhou, Qing, Shi, Jingping
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2017; Springer Nature B.V
• Subjects: Adaptive control; Adaptive control systems; Adaptive systems; Aircraft; Aircraft configurations; Aircraft control; Aircraft performance; Aircraft stability; Aircraft subsystems; Automotive Engineering; Classical Mechanics; Computer simulation; Configuration management; Constraints; Control; Control stability; Control systems; Control theory; Controllers; Dynamical Systems; Engineering; Liapunov functions; Mathematical models; Mechanical Engineering; Morphing aircraft; Neural networks; Nonlinear dynamics; Original Paper; Parameter estimation; Parameter modification; Parameter uncertainty; Parameters; Sliding mode control; Vibration; Barrier Lyapunov functions; Input and output constraints; Minimal learning parameter; Sliding mode differentiator
• ISSN: 0924-090X; 1573-269X
• DOI: 10.1007/s11071-016-3196-0

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.