Lyapunov Function-guided Reinforcement Learning for Flight Control
This work addresses flight control stability for autonomous systems, but appears incremental as it enhances an existing system with respect to action smoothness and convergence analysis.
The paper tackled improving convergence in a cascaded online learning flight control system by analyzing the increment of a Lyapunov function candidate, accounting for discretization and state prediction errors, and presented comparative simulation results.
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.