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Robust Adaptive Control for a Small Unmanned Helicopter Using Reinforcement Learning.

Bin Xian, Xu Zhang, Haonan Zhang

IEEE Transactions on Neural Networks and Learning Systems

|June 14, 2021

Summary

This study introduces a new adaptive controller for unmanned helicopters using reinforcement learning (RL). The controller effectively manages system uncertainties and disturbances for improved flight stability and tracking.

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Area of Science:

Robotics
Control Systems
Artificial Intelligence

Background:

Unmanned helicopters face challenges with system uncertainties and external disturbances.
Adaptive control is crucial for maintaining stability and performance in uncertain environments.

Purpose of the Study:

To develop a novel adaptive controller for small-size unmanned helicopters.
To address system uncertainties and unknown external disturbances using reinforcement learning.

Main Methods:

Utilized reinforcement learning (RL) with actor-critic networks for online estimation and optimization.
Integrated a nonlinear robust component based on sliding mode control for compensation.
Employed Lyapunov-based stability analysis to prove system stability and convergence.

Main Results:

The actor network effectively estimated dynamic unmodeling uncertainties.
The critic network optimized the tracking performance function.
The integrated controller demonstrated robust performance against uncertainties and disturbances.
Real-time experiments validated the controller's effectiveness on a helicopter testbed.

Conclusions:

The proposed RL-based adaptive controller achieves good control performance for unmanned helicopters.
The controller successfully handles system uncertainties and external disturbances.
The methodology ensures closed-loop system stability and asymptotic convergence of attitude tracking errors.