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. 

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-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.

Aiming at the low tracking accuracy and poor fault tolerance of displacement-controlled systems, this paper puts forward a fault tolerant robust controller. The influence of many uncertain factors on trajectory tracking performance have been considered, such as external disturbances, high nonlinearity, system faults, and complex modeling uncertainty of the system. First, a new finite-time observer is proposed to estimate the system states, disturbances, and system faults simultaneously. Then, an improved fast super twisting algorithm is designed to guarantee the reachability of the sliding surface in finite time. Furthermore, the prescribed performance constrained control technique is combined to ensure that the tracking error remains within the prescribed bounded range even though faults occur. Finally, the effectiveness of the proposed control method is validated by simulation results. 

Fault tolerant control of nonlinear hydraulic systems with prescribed performance constraint.

Asynchronous secure controller design for singularly perturbation stochastic semi-Markov jump CPSs with the memory-based dynamic event-triggered scheme against complex cyber-attacks

Observer-based optimal control method combination with event-triggered strategy for hypersonic morphing vehicle

Adaptive sliding mode control of hydraulic systems with the event trigger and finite-time disturbance observer

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Wei Shen

Author Tools

Chat about Author

Papers

Adaptive sliding mode control of hydraulic systems with the event trigger and finite-time disturbance observer