Adaptive control method and system for vertical take-off and landing aircraft based on distributed electric thrust array

By using an adaptive thrust allocation optimization method that senses in real time and dynamically updates the dynamic model, the problem of insufficient adaptability of distributed electric thrust array control methods in complex environments is solved, improving the adaptability and robustness of vertical takeoff and landing aircraft, and realizing high-precision attitude control and mission execution.

CN121455172BActive Publication Date: 2026-07-21HUBEI HANRUIJING AUTOMOBILE INTELLIGENT SYST CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUBEI HANRUIJING AUTOMOBILE INTELLIGENT SYST CO LTD
Filing Date
2025-10-28
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing distributed electric thrust array control methods lack adaptability in complex environments, have limited cooperative control accuracy, poor system robustness, and are unable to cope with dynamic uncertainties such as real-time wind field changes and system component failures, thus affecting the mission performance and reliability of vertical takeoff and landing aircraft.

Method used

By sensing the aircraft's status and environmental information in real time, dynamically updating the dynamic model, and employing adaptive thrust allocation optimization algorithms and system-level robust management, the adaptability and collaborative control accuracy of the aircraft in dynamic mission scenarios are improved.

Benefits of technology

It improves the adaptability and system robustness of vertical takeoff and landing aircraft in complex environments, ensuring high-precision attitude control and mission execution capabilities in dynamic mission scenarios.

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Abstract

The application provides a vertical take-off and landing aircraft adaptive control method and system based on a distributed electric thrust array, real-time sensing data of the vertical take-off and landing aircraft is acquired, current flight state parameters and current environmental disturbance parameters of the vertical take-off and landing aircraft are estimated in real time based on the sensing data, and dynamic model parameters of the vertical take-off and landing aircraft are updated online to obtain an adaptive dynamic model; a task target of the vertical take-off and landing aircraft is acquired, the task target is converted into independent thrust instructions of each electric thrust unit through an adaptive thrust distribution optimization algorithm, and each electric thrust unit is controlled to output thrust. Through high-precision real-time sensing, dynamic model adaptation, multi-source information fusion and robust optimization distribution strategy, the adaptive capability, cooperative control precision and system robustness of the vertical take-off and landing aircraft in a dynamic task scene are improved.
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