9 papers
Stabilization Learning: A Paradigm Transition Bridging Control Theory and Machine Learning
Quan Quan
Stabilization learning is an interdisciplinary paradigm that bridges control theory and machine learning. Its core idea is to enable systems to adjust their policies under perturba…
Energy-Optimal Spatial Iterative Learning within a Virtual Tube
Chen Min, Shuli Lv, Pengda Mao +3
Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned aerial vehicles (UAVs) are s…
Learning to Adapt: Reptile-D-Learning for Robust and Efficient Control Under Parametric Uncertainty
Haipeng Cao, Zhaolong Shen, Quan Quan
Learning-based Lyapunov Control (LLC) provides formal stability guarantees for nonlinear systems, but its validity relies heavily on accurate system models. Parameter variations an…
L-Learning : A Lyapunov-Based Approach Leveraging Lagrangian Mechanics for Efficient and Stable Robot Tracking
Quan Quan, Hao Li
This paper presents L-Learning, a novel data-driven control framework for robotics that integrates Lyapunov stability theory with Lagrangian mechanics to enhance trajectory trackin…
An Efficient Real-Time Planning Method for Swarm Robotics Based on an Optimal Virtual Tube
Pengda Mao, Shuli Lv, Chen Min +2
Robot swarms navigating through unknown obstacle environments are an emerging research area that faces challenges. Performing tasks in such environments requires swarms to achieve…
MSACL: Multi-Step Actor-Critic Learning with Lyapunov Certificates for Exponentially Stabilizing Control
Yongwei Zhang, Yuanzhe Xing, Quanyi Liang +2
For stabilizing control tasks, model-free reinforcement learning (RL) approaches face numerous challenges, particularly regarding the issues of effectiveness and efficiency in comp…