Lyapunov Function-guided Reinforcement Learning for Flight Control

TL;DR

This paper introduces a Lyapunov function-guided reinforcement learning approach to improve the convergence and stability of a cascaded online learning flight control system, emphasizing action smoothness and error analysis.

Contribution

It presents a novel Lyapunov function-based method for analyzing and enhancing reinforcement learning-based flight control systems, considering discretization and prediction errors.

Findings

Improved convergence performance demonstrated in simulations.

Enhanced action smoothness in flight control.

Effective handling of discretization and prediction errors.

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.