Optimal preset time control method for hypersonic vehicle with preset performance

By constructing a speed and altitude subsystem model of a hypersonic vehicle, and combining the Hamilton-Jacobi-Bellman equation and neural network, an optimal virtual controller and adaptive law were designed to solve the trajectory tracking problem of hypersonic vehicles in complex environments, achieving global performance optimization and efficient resource utilization within a preset time.

CN122151909APending Publication Date: 2026-06-05HENAN UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HENAN UNIV OF SCI & TECH
Filing Date
2026-01-15
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Hypersonic vehicles struggle to achieve precise trajectory tracking in highly nonlinear, strongly coupled, and complex environments. Furthermore, existing control methods face stringent time constraints and are difficult to implement in engineering, making it challenging to balance control flexibility with practicality.

Method used

The optimal preset time control method for hypersonic aircraft with preset performance is adopted. By constructing speed and altitude subsystem models, designing predefined time filters and fixed-time preset performance functions, and combining Hamilton-Jacobi-Bellman equations and radial basis function neural networks, the optimal virtual controller and adaptive law are realized. An event triggering mechanism is introduced to optimize the control process.

Benefits of technology

To achieve optimal global system performance within a preset time, improve transient response speed and steady-state tracking accuracy, reduce resource consumption, and enhance the engineering practicality of the control system.

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Abstract

The application discloses a kind of hypersonic aircraft optimal preset time control method with preset performance, it is related to aircraft control technical field.The method decomposes hypersonic aircraft model into speed subsystem and height subsystem;For height subsystem, introduce predefined time filter and construct optimal cost function, based on Hamilton-Jacobi-Bellman equation, using execution-evaluation neural network online solution, obtain optimal virtual controller and height tracking controller;For speed subsystem, first, constrain tracking error by fixed time preset performance function and convert it into equivalent error, based on the equivalent error, construct optimal cost function and Hamilton-Jacobi-Bellman equation, and solve to obtain optimal speed tracking controller;At the same time, introduce event trigger mechanism to reduce control update frequency.The application realizes the accurate preset of convergence time and the global optimization of system comprehensive performance, improves the transient performance and steady-state performance of the system.
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