Model-Free Control for Resource Harvesting in Computing Grids

Guilloteau, Quentin; Robu, Bogdan; Join, Cédric; Fliesś, Michel; Rutten, Éric; Richard, Olivier

doi:10.1109/ccta49430.2022.9966035

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Otherwise, the stability of the system may be deteriorated. In several existing examples, M. Fliess and C. Join indicate that ν may always be chosen quite, i.e., 1 [42][43][44][45][46][47] or 2 [48], and 1 in all concrete situations. For instance, intelligent PID (iPID) is designed with ν = 2, while intelligent PI (iPI) is used with ν = 1.…”

Section: Principle Of the Methodsmentioning

confidence: 99%

Model-free control-based vector control of synchronous reluctance motor

Selma

Bounadja

Belmadani

et al. 2023

Int. J. Dynam. Control

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The capacity to determine how the state variable system would behave during fixed-point and transition operations is a significant enhancement of the model-free control technique. This paper describes a vector control, also called field-oriented control (FOC) based on model-free control approach for a synchronous reluctance motor (SynRM). The high performance of the proposed model-free control (MFC)-based vector control of SynRM drive is evaluated and compared with (FOC) based on classical proportional-integral (PI) controller under diverse conditions. At last, the results reveal the suggested MFC's efficiency and technical advantages, as well as its advantages over the classical PI.

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Section: Principle Of the Methodsmentioning

confidence: 99%

Model-free control-based vector control of synchronous reluctance motor

Selma

Bounadja

Belmadani

et al. 2023

Int. J. Dynam. Control

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“…The MFC framework mentioned in the introduction has been successfully applied in vehicle longitudinal control applications [16] and in lateral control for low-speed Automated Guided Vehicles [17], as well as in other applications [18,19]. Alternatively, in [20], the flatness theory [21], which enables the identification of differentially flat outputs for nonlinear systems, was applied to implement the lateral control of a vehicle together with a model-free feedback controller.…”

Section: State-of-the-art Literaturementioning

confidence: 99%

Speed-Adaptive Model-Free Path-Tracking Control for Autonomous Vehicles: Analysis and Design

et al. 2023

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One of the challenges of autonomous driving is to increase the number of situations in which an intelligent vehicle can continue to operate without human intervention. This requires path-tracking control to keep the vehicle stable while following the road, regardless of the shape of the road or the longitudinal speed at which it is moving. In this work, a control strategy framed in the Model-Free Control paradigm is presented to control the lateral vehicle dynamics in a decoupled control architecture. This strategy is designed to guide the vehicle through trajectories with diverse dynamic constraints and over a wide speed range. A design method for this control strategy is proposed, and metrics for trajectory tracking quality, system stability, and passenger comfort are applied to evaluate the controller’s performance. Finally, simulation and real-world tests show that the developed strategy is able to track realistic trajectories with a high degree of accuracy, safety, and comfort.

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“…Control theory has been used in computing systems, especially in cloud systems [5], [6], and in real-time systems [7] and the Internet of Things [8]. Its use for HPC systems is recent, however.…”

Section: A Control Theory For High-performance Computingmentioning

confidence: 99%

Adaptive Power Control for Sober High-Performance Computing

Hawila

Cerf

Bleuse

et al. 2022

2022 IEEE Conference on Control Technology and Applications (CCTA)

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Soberness-in terms of electrical power-of data centers and high-performance computing systems is becoming an important design issue, as the global energy consumption of information technologies is rising at considerable levels. This issue is all the more complex as these systems are increasingly heterogeneous and variable in their behavior, for example, w.r.t. performance and power consumption, and less predictable, thus demanding runtime management and feedback control.This paper addresses the problem of the control of the power allocated to processors and hence their energy consumption and performance. The use of feedback control allows the energy consumption to be reduced by decreasing the speed without losing performance, by exploiting periods where read/write operations slow the progress. Previous works present limitations regarding both modeling (nonlinear models with numerous parameters) and control performance (mainly instability caused by platform variations). We develop a novel adaptive control that is robust to the variety of execution platforms while maintaining the existing global goals of energy management. We evaluate-on a real system using the Grid'5000 testbed-the robustness of the control to changes in initial parameters and to disturbances, and we compare it with the previous proportionalintegral (PI) control. Our adaptive control approach allows for up to 25% energy savings.

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Model-Free Control for Resource Harvesting in Computing Grids

Cited by 9 publications

References 22 publications

Model-free control-based vector control of synchronous reluctance motor

Model-free control-based vector control of synchronous reluctance motor

Speed-Adaptive Model-Free Path-Tracking Control for Autonomous Vehicles: Analysis and Design

Adaptive Power Control for Sober High-Performance Computing

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