Predefined-Time Tracking Control of Servo Hydraulic Cylinder Based on Reinforcement Learning
Year: 2025
Type: article
Abstract: Electro-hydraulic servo systems are characterized by significant nonlinearities. Reinforcement learning (RL), known for its model-free nature and adaptive learning capabilities, presents a promising approach for handling uncertainties inherent in such systems. This paper proposes a predefined-time tracking control scheme based on RL, which achieves fast and accurate tracking performance. The proposed design employs an actor–critic neural network strategy to actively compensate for system uncertainties. Within a conventional backstepping framework, a command-filtering technique is integrated to construct a predefined-time control structure. This not only circumvents the issue of differential explosion but also guarantees system convergence within a predefined time, which can be specified independently by the designer. Simulation results and comparisons validate the enhanced control performance of the proposed controller.
Source: Actuators
DOI: 10.3390/act15010009
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7117327103