Model-Based Reinforcement Learning Control of Electrohydraulic Position Servo Systems

Even though the unprecedented success of AlphaGo Zero demonstrated reinforcement learning as a feasible complex problem solver, the research on reinforcement learning control of hydraulic systems is still void. We are motivated by the challenges presented in hydraulic systems to develop a new model-...

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Bibliographic Details
Published inIEEE/ASME transactions on mechatronics Vol. 28; no. 3; pp. 1 - 10
Main Authors Yao, Zhikai, Liang, Xianglong, Jiang, Guo-Ping, Yao, Jianyong
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Asymptotic stability
Closed loops
Control systems
Controllers
Dynamic stability
Dynamical systems
Feedback control
Friction
Hydraulic equipment
Hydraulic systems
Neural networks
Performance evaluation
Reinforcement learning
robust control
Servocontrol
Stability analysis
System dynamics
Systems stability
Tracking errors
Uncertainty
Vehicle dynamics
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Summary:Even though the unprecedented success of AlphaGo Zero demonstrated reinforcement learning as a feasible complex problem solver, the research on reinforcement learning control of hydraulic systems is still void. We are motivated by the challenges presented in hydraulic systems to develop a new model-based reinforcement learning controller that achieves high-accuracy tracking at performance level and with asymptotic stability guarantees at system level. In this article, the proposed design consists of two frameworks: A recursive robust integral of the sign of the error (RISE) control approach to providing closed-loop system stability framework, and a reinforcement learning approach with actor-critic structure to dealing with the unknown dynamics or more specifically, the actor neural network is used to reduce the high feedback gain of the recursive RISE control approach by compensating the unknown dynamic while the critic neural network is integrated to improve the control performance by evaluating the filtered tracking error. A theoretical guarantee for the stability of the overall dynamic system is provided by using Lyapunov stability theory. Simulation and experimental results are provided to demonstrate improved control performance of the proposed controller.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2022.3219115