MODELLING AND NOVEL NEURAL NETWORK PREDICTIVE CONTROL OF A FIXED DISPLACEMENT MOTOR SYSTEM CONTROLLED BY AN ELECTROHYDRAULIC PROPORTIONAL VARIABLE DISPLACEMENT PUMP.

Saved in:

Bibliographic Details
Title:	MODELLING AND NOVEL NEURAL NETWORK PREDICTIVE CONTROL OF A FIXED DISPLACEMENT MOTOR SYSTEM CONTROLLED BY AN ELECTROHYDRAULIC PROPORTIONAL VARIABLE DISPLACEMENT PUMP.
Authors:	Wang, Junyan¹, Fan, Xin², Cai, Junyu³ caijy@czust.edu.cn
Source:	Transactions of FAMENA. 2026, Vol. 50 Issue 3, p1-15. 16p.
Subjects:	Predictive control systems, Marquardt algorithm, Simulink (Computer software), Simulation methods & models, Real-time control, Feedback control system stability, Hydraulic motors
Abstract:	This paper proposes a neural network predictive control (NNPC) method to address the challenge of controlling the output speed in a fixed displacement motor (FDM) system controlled by an electrohydraulic proportional variable displacement pump (EHP-VDP). The dynamic response of the system is divided into four segments, and mathematical models are derived for each segment. The system is modelled using over 400 input-output data pairs, with 75% of the data used for training and the remaining 25% for validation. The Levenberg-Marquardt algorithm (LMA) is employed to optimise the neural network, achieving optimal validation performance with a mean square error of 0.0029637 after 258 epochs. This real-time optimisation enables the output speed of the system to accurately track the reference speeds. Simulink simulations are conducted over a 25-second duration covering five reference speed intervals, demonstrating reliable tracking performance. The NNPC method shows competitive performance compared with backpropagation-neural network-proportional integral derivative (BP-NN-PID) and proportional integrative derivative (PID) controllers, achieving the root mean square error (RMSE), the mean absolute error (MAE), and the integral squared error (ISE) values of 6.72, 1.00, and 572.89, respectively. These results indicate that the NNPC offers advantages in fast-response scenarios while maintaining acceptable tracking accuracy. [ABSTRACT FROM AUTHOR]
	Copyright of Transactions of FAMENA is the property of Faculty of Mechanical Engineering and Naval Architecture, University of Zegreb and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	This paper proposes a neural network predictive control (NNPC) method to address the challenge of controlling the output speed in a fixed displacement motor (FDM) system controlled by an electrohydraulic proportional variable displacement pump (EHP-VDP). The dynamic response of the system is divided into four segments, and mathematical models are derived for each segment. The system is modelled using over 400 input-output data pairs, with 75% of the data used for training and the remaining 25% for validation. The Levenberg-Marquardt algorithm (LMA) is employed to optimise the neural network, achieving optimal validation performance with a mean square error of 0.0029637 after 258 epochs. This real-time optimisation enables the output speed of the system to accurately track the reference speeds. Simulink simulations are conducted over a 25-second duration covering five reference speed intervals, demonstrating reliable tracking performance. The NNPC method shows competitive performance compared with backpropagation-neural network-proportional integral derivative (BP-NN-PID) and proportional integrative derivative (PID) controllers, achieving the root mean square error (RMSE), the mean absolute error (MAE), and the integral squared error (ISE) values of 6.72, 1.00, and 572.89, respectively. These results indicate that the NNPC offers advantages in fast-response scenarios while maintaining acceptable tracking accuracy. [ABSTRACT FROM AUTHOR]
ISSN:	13331124
DOI:	10.21278/TOF.503079325