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Nonlinear model predictive energy management of hydrostatic drive transmissions

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Standard

Nonlinear model predictive energy management of hydrostatic drive transmissions. / Backas, Joni; Ghabcheloo, Reza.

julkaisussa: Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, Vuosikerta 233, Nro 3, 01.03.2019, s. 335-347.

Tutkimustuotosvertaisarvioitu

Harvard

Backas, J & Ghabcheloo, R 2019, 'Nonlinear model predictive energy management of hydrostatic drive transmissions', Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, Vuosikerta. 233, Nro 3, Sivut 335-347. https://doi.org/10.1177/0959651818793454

APA

Backas, J., & Ghabcheloo, R. (2019). Nonlinear model predictive energy management of hydrostatic drive transmissions. Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, 233(3), 335-347. https://doi.org/10.1177/0959651818793454

Vancouver

Backas J, Ghabcheloo R. Nonlinear model predictive energy management of hydrostatic drive transmissions. Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering. 2019 maalis 1;233(3):335-347. https://doi.org/10.1177/0959651818793454

Author

Backas, Joni ; Ghabcheloo, Reza. / Nonlinear model predictive energy management of hydrostatic drive transmissions. Julkaisussa: Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering. 2019 ; Vuosikerta 233, Nro 3. Sivut 335-347.

Bibtex - Lataa

@article{30a20d05433a4754a0495c7559754d82,
title = "Nonlinear model predictive energy management of hydrostatic drive transmissions",
abstract = "In this article, we devise a nonlinear model predictive control framework for the energy management of nonhybrid hydrostatic drive transmissions. The controller determines the optimal control commands of the actuators by minimising a cost function over a receding horizon. With our approach, the velocity-tracking error is minimised while keeping the fuel economy of the system high. The hydrostatic drive transmission system studied in this article is a typical commercial work machine, that is, there is no energy storage or alternative power source in the system (a nonhybrid hydrostatic drive transmission). We evaluate success with a validated simulation model of the hydrostatic drive transmission of a municipal tractor. In our experiments, a detailed system model is used both in the system simulation and in the prediction phase of the nonlinear model predictive control. The use of a detailed model in the nonlinear model predictive control framework places our design as a benchmark for controlling nonhybrid hydrostatic drive transmissions, when compared to solutions using simplified models or computationally less intensive control methods as in earlier work by the authors. Our nonlinear model predictive control approach enables numerically robust optimisation convergence with the utilised complex nonlinear model. Above all, this is accomplished with stabilising terminal constraints and distinctive terminal cost, both based on an optimal steady-state solution. In addition, a simple method to generate initial guesses for optimisation is introduced. When compared with the performance of a controller based on quasi-static models, our results show notable improvement in velocity tracking while maintaining high fuel economy. Furthermore, our experiments demonstrate that framing energy management as a nonlinear model predictive control provides a flexible and rigorous framework for fast velocity tracking and high energy efficiency. We also compare the results with those of an industrial baseline controller.",
keywords = "energy efficiency, fluid power, hydraulic systems, power management, Power transmission",
author = "Joni Backas and Reza Ghabcheloo",
year = "2019",
month = "3",
day = "1",
doi = "10.1177/0959651818793454",
language = "English",
volume = "233",
pages = "335--347",
journal = "Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering",
issn = "0959-6518",
publisher = "SAGE Publications",
number = "3",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Nonlinear model predictive energy management of hydrostatic drive transmissions

AU - Backas, Joni

AU - Ghabcheloo, Reza

PY - 2019/3/1

Y1 - 2019/3/1

N2 - In this article, we devise a nonlinear model predictive control framework for the energy management of nonhybrid hydrostatic drive transmissions. The controller determines the optimal control commands of the actuators by minimising a cost function over a receding horizon. With our approach, the velocity-tracking error is minimised while keeping the fuel economy of the system high. The hydrostatic drive transmission system studied in this article is a typical commercial work machine, that is, there is no energy storage or alternative power source in the system (a nonhybrid hydrostatic drive transmission). We evaluate success with a validated simulation model of the hydrostatic drive transmission of a municipal tractor. In our experiments, a detailed system model is used both in the system simulation and in the prediction phase of the nonlinear model predictive control. The use of a detailed model in the nonlinear model predictive control framework places our design as a benchmark for controlling nonhybrid hydrostatic drive transmissions, when compared to solutions using simplified models or computationally less intensive control methods as in earlier work by the authors. Our nonlinear model predictive control approach enables numerically robust optimisation convergence with the utilised complex nonlinear model. Above all, this is accomplished with stabilising terminal constraints and distinctive terminal cost, both based on an optimal steady-state solution. In addition, a simple method to generate initial guesses for optimisation is introduced. When compared with the performance of a controller based on quasi-static models, our results show notable improvement in velocity tracking while maintaining high fuel economy. Furthermore, our experiments demonstrate that framing energy management as a nonlinear model predictive control provides a flexible and rigorous framework for fast velocity tracking and high energy efficiency. We also compare the results with those of an industrial baseline controller.

AB - In this article, we devise a nonlinear model predictive control framework for the energy management of nonhybrid hydrostatic drive transmissions. The controller determines the optimal control commands of the actuators by minimising a cost function over a receding horizon. With our approach, the velocity-tracking error is minimised while keeping the fuel economy of the system high. The hydrostatic drive transmission system studied in this article is a typical commercial work machine, that is, there is no energy storage or alternative power source in the system (a nonhybrid hydrostatic drive transmission). We evaluate success with a validated simulation model of the hydrostatic drive transmission of a municipal tractor. In our experiments, a detailed system model is used both in the system simulation and in the prediction phase of the nonlinear model predictive control. The use of a detailed model in the nonlinear model predictive control framework places our design as a benchmark for controlling nonhybrid hydrostatic drive transmissions, when compared to solutions using simplified models or computationally less intensive control methods as in earlier work by the authors. Our nonlinear model predictive control approach enables numerically robust optimisation convergence with the utilised complex nonlinear model. Above all, this is accomplished with stabilising terminal constraints and distinctive terminal cost, both based on an optimal steady-state solution. In addition, a simple method to generate initial guesses for optimisation is introduced. When compared with the performance of a controller based on quasi-static models, our results show notable improvement in velocity tracking while maintaining high fuel economy. Furthermore, our experiments demonstrate that framing energy management as a nonlinear model predictive control provides a flexible and rigorous framework for fast velocity tracking and high energy efficiency. We also compare the results with those of an industrial baseline controller.

KW - energy efficiency

KW - fluid power

KW - hydraulic systems

KW - power management

KW - Power transmission

U2 - 10.1177/0959651818793454

DO - 10.1177/0959651818793454

M3 - Article

VL - 233

SP - 335

EP - 347

JO - Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering

JF - Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering

SN - 0959-6518

IS - 3

ER -