Design, Dynamic Modeling, and Experimental Validation of A Novel Alternating Flow Variable Displacement Hydraulic Pump

Li, Mengtang; Foss, Ryan; Stelson, Kim A.; Ven, James D. Van de; Barth, Eric J.

doi:10.1109/tmech.2019.2906859

Cited by 29 publications

(18 citation statements)

References 13 publications

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“…This application is of direct relevance in various industrial systems, e.g. compressors [32], hydraulic pumps [33], weaving looms [34] and presses [35].…”

Section: Resultsmentioning

confidence: 99%

Adaptive control of a mechatronic system using constrained residual reinforcement learning

Staessens¹,

Lefebvre²,

Crevecoeur³

2021

Preprint

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We propose a simple, practical and intuitive approach to improve the performance of a conventional controller in uncertain environments using deep reinforcement learning while maintaining safe operation. Our approach is motivated by the observation that conventional controllers in industrial motion control value robustness over adaptivity to deal with different operating conditions and are suboptimal as a consequence. Reinforcement learning on the other hand can optimize a control signal directly from input-output data and thus adapt to operational conditions, but lacks safety guarantees, impeding its use in industrial environments. To realize adaptive control using reinforcement learning in such conditions, we follow a residual learning methodology, where a reinforcement learning algorithm learns corrective adaptations to a base controller's output to increase optimality. We investigate how constraining the residual agent's actions enables to leverage the base controller's robustness to guarantee safe operation. We detail the algorithmic design and propose to constrain the residual actions relative to the base controller to increase the method's robustness. Building on Lyapunov stability theory, we prove stability for a broad class of mechatronic closed-loop systems. We validate our method experimentally on a slider-crank setup and investigate how the constraints affect the safety during learning and optimality after convergence.

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“…This application is of direct relevance in various industrial systems, e.g. compressors [32], hydraulic pumps [33], weaving looms [34] and presses [35].…”

Section: Resultsmentioning

confidence: 99%

Adaptive control of a mechatronic system using constrained residual reinforcement learning

Staessens¹,

Lefebvre²,

Crevecoeur³

2021

Preprint

View full text Add to dashboard Cite

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“…At the University of Minnesota, they have worked on an adjustable linkage concept for both pumps and motors that they claim has the potential to be more efficient than conventional control [48], [49]. Another interesting concept is the so-called alternating flow pump, in which two pistons are connected and the phase between them is controlled [50]. Back to the conventional methods.…”

Section: Displacement Element Capacitymentioning

confidence: 99%

On Electrified Fluid Power Systems in Mobile Machinery

Kärnell

2022

Linköping Studies in Science and Technology. Dissertations

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High power density in combination with flexible power distribution possibilities and extreme robustness are reasons why fluid power has been the preferred technology in mobile machinery, such as excavators and cranes, since the mid-20th century. In principle, the machines have been powered by a combustion engine which powers a pump, with the output from the pump being distributed to different functions via valves. However, a transformation is currently underway. Combustion engines are being replaced by electric motors, and batteries able to store energy corresponding to several hours of operation are often desired. Since batteries tend to be heavy and expensive, reducing the energy consumption is getting higher priority than ever before. There are applications where electrification means that hydraulic components are replaced by electric counterparts, but fluid power has characteristics that are highly desirable in mobile machinery. Therefore, many hydraulic actuators will remain. Conventional hydraulic systems, which are known for their inefficiency, should, however, be adapted to the new conditions brought about by electrification. The question, and the overall subject of this thesis, is: how? The research has focused on two main topics: pump-controlled systems, which are systems where each actuator has its own supply unit, and the use of variable displacement pumps in electrified systems.A large proportion of the losses in many conventional hydraulic systems is due to the simultaneous operation of functions that require different pressure levels. One way to avoid these losses is to use pump-controlled systems. How these systems should be designed is, however, far from obvious. In this thesis, different types of pump-controlled systems are compared, both statically and dynamically.Regarding variable displacement pumps, they have had a natural place in many conventional systems, but electrification may change this, since speedcontrol can now also be used for flow-and pressure control. However, there are still aspects relating to energy consumption and component dimensioning, among other things, that makes variable pumps relevant. These aspects are investigated here, and different types of variable pumps are reviewed.Linköping, August 2022 Samuel Kärnell v vi PapersThe following publications are included in the thesis and can be regarded as its foundation. They will be referred to by their Roman numerals. Apart from formatting changes, they are reproduced in their original form. Notice that they are not appended in chronological order. Instead, they are sorted based on the topics; the first three focus on displacement control and the last two on pump-controlled systems.[I] S. Kärnell and L. Ericson, "Classification and review of variable displacement fluid power pumps and motors," International Journal of Fluid Power (submitted), 2022.[II] S. Kärnell, A. Rankka, A. Dell'Amico, and L. Ericson, "Digital pumps in speed-controlled systems -an energy study for a loader crane application," in

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“…We consider a slider-crank mechanism translating a rotary motion into a linear displacement that is subject to an unidentified load. This application is of direct relevance in various industrial applications such as compressors [28], hydraulic pumps [29], weaving looms [30] and presses [31]. A system model comprehending the dynamics of the slider-crank system is built and experimental data is collected from a practical setup.…”

Section: Slider-crank Systemmentioning

confidence: 99%

Neural Network Augmented Physics Models for Systems with Partially Unknown Dynamics: Application to Slider-Crank Mechanism

De Groote,

Kikken,

Hostens

et al. 2019

Preprint

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Mechatronic systems are plagued by nonlinearities and contain uncertainties due to, amongst others, interactions with their environment. Models exhibiting accurate multistep predictive capabilities can be valuable in the context of motion control and design of servo controlled systems. Neural Network Augmented Physics (NNAP) models are presented in this paper to comprehend the behavior of servo systems that contain partially unknown dynamics. By means of a hybrid modeling formalism, neural network models are closely merged with physics-based state-space derivative functions. The methodology is applied on an experimental slider-crank system consisting of a servo drive and mechanical links of which the physical behavior is only partially known. Its interaction with the environment due to a compression spring load and friction phenomena, however, remains unknown. Accurate prediction capabilities of the presented NNAP models are demonstrated on the slider-crank system. Compared to the feedforward modeling formalism, recurrent NNAP models had the ability to even further reduce prediction errors up to 33.4% on validation data. From the converged dynamic NNAP model we extracted the neural network and identified the unknown phenomena, being the spring characteristic and the friction forces, within the mechatronic system.

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Design, Dynamic Modeling, and Experimental Validation of A Novel Alternating Flow Variable Displacement Hydraulic Pump

Cited by 29 publications

References 13 publications

Adaptive control of a mechatronic system using constrained residual reinforcement learning

Adaptive control of a mechatronic system using constrained residual reinforcement learning

On Electrified Fluid Power Systems in Mobile Machinery

Neural Network Augmented Physics Models for Systems with Partially Unknown Dynamics: Application to Slider-Crank Mechanism

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