arxiv_ai 85% Match Research Paper Control Engineers,Robotics Researchers,Aerospace Engineers 1 week ago

Lyapunov Function-guided Reinforcement Learning for Flight Control

reinforcement-learning › robotics-rl

📄 Abstract

Abstract: A cascaded online learning flight control system has been developed and enhanced with respect to action smoothness. In this paper, we investigate the convergence performance of the control system, characterized by the increment of a Lyapunov function candidate. The derivation of this metric accounts for discretization errors and state prediction errors introduced by the incremental model. Comparative results are presented through flight control simulations.

Authors (2)

Yifei Li

Erik-Jan van Kampen

Submitted

October 26, 2025

arXiv Category

cs.AI

arXiv PDF

Key Contributions

This paper introduces a Lyapunov function-guided reinforcement learning approach to enhance the action smoothness and convergence performance of cascaded online learning flight control systems. The method accounts for discretization and state prediction errors, leading to improved control system behavior.

Business Value

Improved safety and efficiency in autonomous flight systems, potentially leading to more reliable drone operations and advanced aircraft control.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

Moderate. Requires robust simulation environments and careful tuning for real-world flight conditions.

Limitations Addressed

Action smoothness and convergence performance issues in existing online learning flight control systems.

Technical Tags

reinforcement learningLyapunov functionflight controlonline learningcontrol systemaction smoothnessconvergence performancediscretization errorsstate prediction errorsincremental model

Research Topics

Control TheoryReinforcement LearningAutonomous SystemsMachine LearningAerospace Engineering

Methods & Architectures

Lyapunov function-guided reinforcement learningonline learningincremental model

Applications & Tasks

Aerospace Robotics Control OptimizationSystem Convergence Flight Control

Related Fields

Control TheoryAerospace EngineeringRoboticsMachine Learning

Keywords

Reinforcement LearningFlight ControlLyapunov FunctionOnline LearningControl SystemsAutonomous FlightConvergenceAction SmoothnessAerospaceRoboticsMachine LearningControl Theory

Academic Context

#Control Theory#Reinforcement Learning#Autonomous Systems#Machine Learning#Aerospace Engineering

Commercial Potential

Potential Products

Advanced flight control softwareAutonomous drone systems

Target Industries

AerospaceDefenseLogistics

Use Case Examples

Autonomous aerial vehicle controlStabilization of aircraft during complex maneuvers

Competitive Edge

Offers a more theoretically grounded approach to RL for control by incorporating Lyapunov stability, potentially outperforming methods that lack such guarantees.

Resource Requirements

Compute Needs

Moderate, primarily for simulation and training.

Data Requirements

Flight simulation data.

Deployment Constraints

Real-time performance, safety critical systems, environmental variability.

Scalability

Scalability depends on the complexity of the flight dynamics and the control objectives.

Regulatory Considerations

Safety certifications for aerospace applications.

Production Readiness

Maturity Level

Research

Time to Market

Long-term, due to safety-critical nature of aerospace.

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