Journal ArticleDOI
Accurate pressure prediction of a servo-valve controlled hydraulic system
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TLDR
In this article, a structured neural network model is proposed to capture the pressure dynamics of a nonlinear hydraulic system, and the proposed network model could be easily trained to predict the pressure dynamic of an experimental hydraulic test setup provided that the training session is initiated with the weights of the developed model.About:
This article is published in Mechatronics.The article was published on 2012-10-01. It has received 32 citations till now. The article focuses on the topics: Hydraulic machinery & Hydraulic cylinder.read more
Citations
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Journal ArticleDOI
Analytical energy dissipation in large and medium-sized hydraulic press
TL;DR: An analytical approach for calculating energy flow in LMHP system is proposed in order to find out the root of low energy efficiency and a practical simplification method is proposed forLMHP system's energy flow model according to LM HP system's structural features.
Journal ArticleDOI
Physics-of-failure models of erosion wear in electrohydraulic servovalve, and erosion wear life prediction method
TL;DR: A series of physics-of-fail (PoF) models for particle erosion wear of electrohydraulic servovalves (EHSV), and the PoF based erosion wear service life prediction models, are established in this article.
Journal ArticleDOI
Robust MPC–PIC force control for an electro-hydraulic servo system with pure compressive elastic load
TL;DR: This paper presents a state-space representation of model predictive control (MPC) for accurate force tracking of an electro-hydraulic servo system (EHSS), and the control performance is compared between and among PIC, MPC, and hybrid MPC–PIC controller.
Journal ArticleDOI
Pressure prediction on a variable-speed pump controlled hydraulic system using structured recurrent neural networks
TL;DR: In this article, the authors present a study to predict the pressures in the cylinder chambers of a variable-speed pump controlled hydraulic system using structured recurrent neural network topologies where the rotational speed of the pumps, the position and the average velocity of the hydraulic actuator are used as their inputs.
Journal ArticleDOI
RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit
TL;DR: In this paper, a new force tracking control algorithm of partial actuated lower limb exoskeleton suit, which is designed for enhancing human motion is presented, based on a mathematical model of the electro-hydraulic servo system.
References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI
Learning long-term dependencies with gradient descent is difficult
TL;DR: This work shows why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases, and exposes a trade-off between efficient learning by gradient descent and latching on information for long periods.
Journal ArticleDOI
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams,David Zipser +1 more
TL;DR: The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks.
Book
Hydraulic Control Systems
H. E. Merritt,Victor Pomper +1 more
TL;DR: In this article, the authors present an overview of the properties of hydraulic fluids and hydraulic power in control systems, including pressure and flow control Valves, and hydraulic pumps and motors.
Journal ArticleDOI
Wavelet networks
Qinghua Zhang,Albert Benveniste +1 more
TL;DR: A wavelet network concept, which is based on wavelet transform theory, is proposed as an alternative to feedforward neural networks for approximating arbitrary nonlinear functions.
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