This research introduces an advanced flight control system for optimizing autonomous aircraft performance, leveraging deep reinforcement learning (DRL) to address the complexities of nonlinear flight dynamics. Using a six-degree-of-freedom (6-DoF) rigid aircraft flight dynamics model, we develop a Deep Deterministic Policy Gradient (DDPG) controller tailored for waypoint navigation and attitude stabilization tasks. A custom reward framework and extensive hyperparameter tuning enable effective training within a high-fidelity MATLAB/Simulink environment, achieving high rewards and precise control. Although computationally intensive, the simulations demonstrate robust performance across diverse flight conditions, with potential for real-world applications and future extensions to multi-agent scenarios.
Reinforcement Learning Based Optimization of Autonomous Aircraft Performance
2025-02-24
1157028 byte
Conference paper
Electronic Resource
English
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