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Reinforcement Learning-Based Optimal Formation Tracking for UAVs With Safety Constraints.

Ping Wang, Chengpu Yu, Fang Deng

IEEE Transactions on Neural Networks and Learning Systems

|January 1, 2026

Summary

This study presents a new method for safe optimal formation tracking in multiple fixed-wing uncrewed aerial vehicles (UAVs), addressing disturbances and control limits. The approach ensures collision avoidance and robust performance using reinforcement learning.

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Area of Science:

Robotics and Control Systems
Aerospace Engineering
Artificial Intelligence

Background:

Multiple fixed-wing uncrewed aerial vehicles (UAVs) face challenges in safe optimal formation tracking due to external disturbances and asymmetric control constraints.
Ensuring collision avoidance and maintaining formation integrity are critical for UAV operations.

Purpose of the Study:

To develop a robust control scheme for safe optimal formation tracking of multiple fixed-wing UAVs.
To address external disturbances and asymmetric control constraints effectively.
To guarantee safety constraints, particularly collision avoidance.

Main Methods:

A novel control barrier function (CBF) is designed to characterize and ensure safety constraints.
The problem is transformed into a constrained zero-sum (ZS) differential game with a nonquadratic cost function.
A critic-only reinforcement learning (RL) strategy with experience replay is employed to learn a robust safe Nash policy.

Main Results:

The proposed scheme successfully integrates CBF into the cost function to penalize unsafe behavior.
A damping coefficient is introduced to balance optimality and safety.
The stability and forward invariance of the safe set are verified through simulations.

Conclusions:

The developed control scheme effectively tackles the safe optimal formation tracking problem for multiple fixed-wing UAVs under challenging conditions.
The integration of CBF, ZS differential games, and RL provides a robust and safe solution.
Simulation results validate the effectiveness and reliability of the proposed control strategy.