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Performance improvement-oriented reentry attitude control for reusable launch vehicles with overload constraint.

Xiyu Gu1, Jianguo Guo1, Zongyi Guo1

  • 1Institute of Precision Guidance and Control, Northwestern Polytechnical University, Xi'an 710072, China.

ISA Transactions
|November 21, 2021
PubMed
Summary

This study introduces a new control scheme for reusable launch vehicle reentry, ensuring overload limits are met while improving transient performance. The simplified model enhances practical implementation for attitude control.

Keywords:
Overload constraintPerformance improvementReentry controlReusable launch vehicle

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Area of Science:

  • Aerospace Engineering
  • Control Systems Theory
  • Reentry Vehicle Dynamics

Background:

  • Reusable launch vehicles (RLVs) require robust attitude control during reentry.
  • Overload constraints are critical for vehicle safety and structural integrity.
  • Existing control methods may not adequately balance performance and constraint satisfaction.

Purpose of the Study:

  • To develop a novel control scheme for RLV reentry attitude control.
  • To guarantee adherence to overload constraints throughout the reentry phase.
  • To enhance transient performance compared to traditional methods.

Main Methods:

  • Incorporating overload constraints directly into performance functions.
  • Designing a specialized performance function for improved transient response.
  • Reducing the attitude control model to a first-order system with disturbances.
  • Addressing practical implementation aspects, including control moment allocation.

Main Results:

  • The proposed control law effectively guarantees that overload limitations are not violated.
  • The new control scheme achieves superior transient performance.
  • The simplified first-order system model reduces structural complexity.
  • Simulation results confirm the effectiveness of the proposed approach.

Conclusions:

  • The developed control scheme offers a viable solution for RLV reentry attitude control.
  • The method successfully integrates performance optimization with critical constraint satisfaction.
  • The simplified model facilitates easier practical engineering implementation.