Composite adaptive neural control for automatic carrier landing system with input saturation and output constraints

• Published in: Journal of the Franklin Institute 2024-11, Vol.361 (17), p.107218, Article 107218
• Main Authors: Liu, Yu, Zhang, Yuanyuan, Li, Renfu
• Format: Article
• Language: English
• Subjects: Automatic carrier landing system; Input saturation; Neural networks; Output constraint; Time-varying barrier Lyapunov function
• ISSN: 0016-0032
• DOI: 10.1016/j.jfranklin.2024.107218

This paper investigates the automatic carrier landing control problem in the presence of model uncertainty, airwake disturbances, input saturation, and output constraints. Considering the performance requirements of the carrier-based aircraft, a composite adaptive neural controller is proposed based on the time-varying barrier Lyapunov function and backstepping control techniques. The radial basis function neural network is used to approximate the model uncertainty, where the neural network weight update law incorporating prediction and tracking errors further improves the convergence rate of the neural network and mitigates high-frequency oscillations. Furthermore, an adaptive disturbance compensation model is established to mitigate the adverse effects of airwake disturbances and estimation errors in the neural network. Based on the Lyapunov stability theory, it is proven that the proposed controller maintains the aircraft trajectory within the prescribed constraints and also ensures that all signals in the closed-loop control system are semiglobally uniformly ultimately bounded. Finally, comparative simulations are performed to demonstrate the effectiveness and superiority of the proposed composite adaptive neural control method. •A composite adaptive neural control method is proposed for the automatic carrier landing system with input saturation and output constraints.•The time-varying barrier Lyapunov function is used to ensure that the system meets the output constraints, limiting the aircraft’s flight path to a specified range.•A composite adaptive law is proposed to update the weights of the NNs, which reduces high frequency oscillations during the NN learning process.•By compensating for airwake disturbances and NN estimation errors, the proposed adaptive disturbance compensation model effectively enhances the system’s robustness.