.png){kind=icon}
.png){kind=icon}
Return
A Novel Reinforcement Learning-Based Leveling Control Method for Multi-cylinder Hydraulic Press: Lyapunov Distributional Soft Actor-Critic
.png){kind=icon}
PRE
AI
DOI：10.1007/s40313-026-01276-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a hydraulic press slider leveling control method based on the Lyapunov Distributional Soft Actor-Critic (LDSAC) algorithm, aiming to simultaneously address the critical issues of Q-value overestimation in reinforcement learning and system stability assurance. The LDSAC algorithm innovatively integrates the distributional reinforcement learning framework with Lyapunov stability theory, effectively suppressing Q-value overestimation through distributional Q-value estimation and ensuring global stability of the control system policy by introducing Lyapunov constraints. This paper first establishes a dynamic model for hydraulic press slider leveling, then designs an LDSAC-based control framework, and validates the algorithm's effectiveness by simulations. The results demonstrate that, compared to traditional methods, the LDSAC algorithm exhibits significant advantages in leveling accuracy and Q-value overestimation suppression. In addition, we provide a theoretical analysis showing that the system can achieve finite-time convergence under the learned policy under specific assumptions. This study offers theoretical insights and a potential framework for the intelligent control of complex industrial systems, though further validation in real-world settings is needed.
Keywords:
Reinforcement learning
Lyapunov distributed soft Actor-Critic (LDSAC)
Hydraulic slider leveling
Q-value overestimation suppression
Lyapunov stability
Journal
J
IF:
1.3
Papers:
92
Citations:
1.1K
Organization
Cited Papers