DL-Clip: Online D-Learning with Clipping Operation for Fast Model-Free Stabilizing Control

Abstract

In this paper, we present DL-Clip, an innovative online learning approach for nonlinear stabilizing control that operates without prior knowledge of system dynamics or reward signals, while significantly improving training efficiency. DL-Clip introduces a novel integration of stabilizing control with efficient Reinforcement Learning (RL) training mechanisms. The algorithm uses Lyapunov functions to ensure system stability and employs clipping operations to optimize policy updates, achieving faster convergence. We evaluate the effectiveness of DL-Clip through experiments, including simulations of the inverted pendulum and the Image-Based Visual Servoing (IBVS) for multicopter position stabilization. In addition, we validate the approach through a real flight experiment based on the IBVS problem, demonstrating its practical applicability.