Work

Dynamic Surface Adaptive Control for Air-Breathing Hypersonic Vehicles Based on RBF Neural Networks

Year: 2025

Type: article

Abstract: This paper focuses on the issue of unmodeled dynamics and large-range parametric uncertainties in air-breathing hypersonic vehicles (AHV), proposing an adaptive dynamic surface control method based on radial basis function (RBF) neural networks. First, the hypersonic longitudinal model is transformed into a strict-feedback control system with model uncertainties. Then, based on backstepping control theory, adaptive dynamic surface controllers incorporating RBF neural networks are designed separately for the velocity and altitude channels. The proposed controller achieves three key functions: (1) preventing “differential explosion” through low-pass filter design; (2) approximating uncertain model components and unmodeled dynamics using RBF neural networks; (3) enabling real-time adjustment of controller parameters via adaptive methods to accomplish online estimation and compensation of system uncertainties. Finally, stability analysis proves that all closed-loop system signals are semi-globally uniformly bounded (SGUB), with tracking errors converging to an arbitrarily small residual set. The simulation results indicate that the proposed control method reduces steady-state error by approximately 20% compared to traditional controllers.

Source: Aerospace

DOI: 10.3390/aerospace12110984

Authors: Ouxun Li, Li Deng

Institutions: Chengdu Technological University, University of Electronic Science and Technology of China

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China