Title: Linear active disturbance rejection control of fighter aircraft based on MADDPG algorithm

Authors: Yetong Lin; Yuehui Ji; Yu Song; Junjie Liu

Addresses: Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, 300384, China ' Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, 300384, China ' Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, 300384, China ' Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, 300384, China

Abstract: To address nonlinearity, strong coupling, and disturbances in fighter aircraft attitude control, this paper proposes an intelligent control method based on multi-agent deep deterministic policy gradient (MADDPG) and linear active disturbance rejection control (LADRC). A three-channel LADRC controller estimates and compensates for disturbances, uncertainties, and coupling terms in real time. To overcome the dimensionality curse in multi-parameter optimization, an independent DDPG controller is assigned to each channel, ensuring cooperative control via reward sharing and enhancing dynamic response and disturbance rejection. Additionally, long short-term memory (LSTM) and self-attention mechanisms are integrated into the policy and value networks to improve representation and decision-making. A piecewise combined reward function mitigates sparse rewards. Simulations show that, compared to MADQN-based and traditional LADRC methods, the proposed approach achieves superior control performance and significantly reduces manual parameter tuning efforts.

Keywords: fighter aircraft; attitude control; deep reinforcement learning; long-short-term memory; LSTM; self-attention.

DOI: 10.1504/IJSCC.2025.145787

International Journal of Systems, Control and Communications, 2025 Vol.16 No.2, pp.152 - 172

Received: 26 Nov 2024
Accepted: 15 Feb 2025
Published online: 23 Apr 2025 *

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