Hydraulic turbine system identification and predictive control based on GASA-BPNN

International Journal of Minerals Metallurgy and Materials ◽

10.1007/s12613-021-2290-6 ◽

2021 ◽

Vol 28 (7) ◽

pp. 1240-1247

Author(s):

Xiao-ping Jiang ◽

Zi-ting Wang ◽

Hong Zhu ◽

Wen-shuai Wang

Keyword(s):

System Identification ◽

Predictive Control ◽

Hydraulic Turbine

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Long Short-term Memory Neural Network-based System Identification and Augmented Predictive Control of Piezoelectric Actuators for Precise Trajectory Tracking

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2021.11.150 ◽

2021 ◽

Vol 54 (20) ◽

pp. 38-45

Author(s):

Mayur S. Patil ◽

Bharat Charuku ◽

Juan Ren

Keyword(s):

Neural Network ◽

System Identification ◽

Predictive Control ◽

Trajectory Tracking ◽

Short Term Memory ◽

Piezoelectric Actuators ◽

Term Memory ◽

Long Short Term Memory

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System identification and robust position control for electro-hydraulic servo system using hybrid model predictive control

Journal of Vibration and Control ◽

10.1177/1077546317721417 ◽

2017 ◽

Vol 24 (18) ◽

pp. 4145-4159 ◽

Author(s):

Hai-Bo Yuan ◽

Hong-Cheol Na ◽

Young-Bae Kim

Keyword(s):

System Identification ◽

Model Predictive Control ◽

Predictive Control ◽

Position Control ◽

Servo System ◽

Model Parameters ◽

Integral Control ◽

Hybrid Controller ◽

Hydraulic Servo ◽

Hydraulic Servo System

This paper examined system identification using grey-box model estimation and position-tracking control for an electro-hydraulic servo system (EHSS) using hybrid controller composed of proportional-integral control (PIC) and model predictive control (MPC). The nonlinear EHSS model is represented by differential equations. We identify model parameters and verify their accuracy against experimental data in MATLAB to evaluate the validity of this mathematical model. To guarantee improved performance of EHSS and precision of cylinder position, we propose a hybrid controller composed of PIC and MPC. The controller is designed using the Control Design and Simulation module in the Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW). A LabVIEW-based experimental rig is developed to apply the proposed controller in real time. Then, the validity and performance superiority of the hybrid controller were confirmed by comparing them with the MPC and PIC results. Results of real-life experiments show improved robustness and dynamic and static properties of EHSS.

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Model Predictive Control oriented experiment design for system identification: A graph theoretical approach

Journal of Process Control ◽

10.1016/j.jprocont.2017.02.001 ◽

2017 ◽

Vol 52 ◽

pp. 75-84 ◽

Author(s):

Afrooz Ebadat ◽

Patricio E. Valenzuela ◽

Cristian R. Rojas ◽

Bo Wahlberg

Keyword(s):

System Identification ◽

Model Predictive Control ◽

Predictive Control ◽

Theoretical Approach ◽

Experiment Design ◽

Graph Theoretical Approach

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Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification

Neurocomputing ◽

10.1016/j.neucom.2018.04.060 ◽

2018 ◽

Vol 308 ◽

pp. 101-110 ◽

Author(s):

Ping Zhou ◽

Dongwei Guo ◽

Tianyou Chai

Keyword(s):

Blast Furnace ◽

System Identification ◽

Predictive Control ◽

Molten Iron ◽

Inverse System ◽

Data Driven ◽

Blast Furnace Ironmaking ◽

Molten Iron Quality

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Approximate Explicit Model Predictive Control via Piecewise Nonlinear System Identification

SSRN Electronic Journal ◽

10.2139/ssrn.3315784 ◽

2019 ◽

Author(s):

Van Vuong Trinh ◽

Mazen Alamir ◽

Patrick Bonnay

Keyword(s):

System Identification ◽

Nonlinear System ◽

Model Predictive Control ◽

Predictive Control ◽

Nonlinear System Identification ◽

Explicit Model ◽

Explicit Model Predictive Control

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Introducing System Identification Strategy into Model Predictive Control

Journal of Systems Science and Complexity ◽

10.1007/s11424-020-9058-3 ◽

2020 ◽

Vol 33 (5) ◽

pp. 1402-1421

Author(s):

Jianhong Wang ◽

A. Ramirez-Mendoza Ricardo ◽

de J Lozoya Santos Jorge

Keyword(s):

System Identification ◽

Model Predictive Control ◽

Predictive Control ◽

Identification Strategy

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Nonlinear System Identification in Frequent and Infrequent Operating Points for Nonlinear Model Predictive Control

Information Technology And Control ◽

10.5755/j01.itc.42.1.997 ◽

2013 ◽

Vol 42 (1) ◽

Author(s):

Alireza Fatehi ◽

Behnaneh Sadeghpour ◽

Batool Labibi

Keyword(s):

System Identification ◽

Nonlinear System ◽

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Nonlinear System Identification ◽

Nonlinear Model Predictive Control ◽

Operating Points

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Nonlinear system identification and predictive control of a heat exchanger based on local linear fuzzy models

Proceedings of the 1997 American Control Conference (Cat. No.97CH36041) ◽

10.1109/acc.1997.612074 ◽

1997 ◽

Author(s):

O. Hecker ◽

O. Nelles ◽

O. Moseler

Keyword(s):

Heat Exchanger ◽

System Identification ◽

Nonlinear System ◽

Predictive Control ◽

Nonlinear System Identification ◽

Fuzzy Models ◽

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System identification and predictive control of a building heating system with multiple boilers

2011 24th Canadian Conference on Electrical and Computer Engineering(CCECE) ◽

10.1109/ccece.2011.6030710 ◽

2011 ◽

Author(s):

Muhammad Awais Abbas ◽

Ryan Naughton ◽

J. Mikael Eklund

Keyword(s):

System Identification ◽

Predictive Control ◽

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Data-driven Model Predictive Control for Lean NO x Trap Regeneration * *This work has been carried out within the project “Experiment design in nonlinear system identification with application to automotive problems”, funded by General Motors Global Propulsion Systems.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2017.08.1436 ◽

2017 ◽

Vol 50 (1) ◽

pp. 6004-6009 ◽

Author(s):

Milad Karimshoushtari ◽

Carlo Novara ◽

Antonino Trotta

Keyword(s):

System Identification ◽

Nonlinear System ◽

Model Predictive Control ◽

Predictive Control ◽

Nonlinear System Identification ◽

Experiment Design ◽

Data Driven ◽

General Motors ◽

Propulsion Systems

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