Cooperative Extremum Seeking Control via Sliding Mode for Distributed Optimization

Salamah, Yasser Bin; Fiorentini, Lisa; Ozguner, Jmit

doi:10.1109/cdc.2018.8619043

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…One of the main contributions of this paper is the development of a model-free optimization method via distributed sliding-mode-based ES for wind farm optimization. This paper extends, from the application point of view, the results presented in [25]. Compared with the existing works, the proposed solution is distinguishable in several aspects: First, it is an extension to the centralized multivariable ES control schemes proposed in [12] for wind farm optimization problems.…”

Section: Introductionmentioning

confidence: 74%

“…Although the centralized ES approach has proven to be effective in many areas, distributed optimization approaches and decentralized schemes improve the scalability of systems. Second, we compared with the results proposed in [26] to find Nash solution in a non-cooperative game and considering that the Nash solution is not the socially optimal solution, this paper seeks to extend the result using the newly developed cooperative ES control [25] to solve the optimization problem in a cooperative fashion. Third, in contrast to existing works in distributed ES [27][28][29] and wind farm optimization [21,22,24,30] that build on the sinusoidal-perturbation approach, this work employs a sliding-mode-based ES control.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Extremum-Seeking for Wind Farm Power Maximization Using Sliding Mode Control

Salamah

Özgüner

2021

Energies

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This paper introduces a sliding-mode-based extremum-seeking algorithm aimed at generating optimal set-points of wind turbines in wind farms. A distributed extremum-seeking control is directed to fully utilize the captured wind energy by taking into consideration the wake and aerodynamic properties between wind turbines. The proposed approach is a model-free algorithm. Namely, it is independent of the model selection of the wake interaction between the wind turbines. The proposed distributed scheme consists of two parts. A dynamic consensus algorithm and an extremum-seeking controller based on sliding-mode theory. The distributed consensus algorithm is exploited to estimate the value of the total power produced by a wind farm. Subsequently, sliding-mode extremum-seeking controllers are intended to cooperatively produce optimal set-points for wind turbines within the farm. Scheme performance is tested via extensive simulations under both steady and varying wind speed and directions. The presented distributed scheme is compared with a centralized approach, in which the problem can be seen as a multivariable optimization. The results show that the employed scheme is able to successfully maximize power production in wind farms.

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Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Distributed Extremum-Seeking for Wind Farm Power Maximization Using Sliding Mode Control

Salamah

Özgüner

2021

Energies

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“…A proportionalintegral extremum seeking design technique is proposed in [7], while, in [8], the gradient is approximated through a realtime parameter estimation. In [9], authors propose the use of the sliding mode to generate the dither signal at the base of The authors are with the Department of Electrical and Information Engineering, "Guglielmo Marconi", University of Bologna, 40126 Bologna, Italy, e-mail: {nicola.mimmo2, guido.carnevale, a.testa, giuseppe.notarstefano}@unibo.it. This result is part of a project that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 638992 -OPT4SMART).…”

Section: Introductionmentioning

confidence: 99%

Extremum Seeking Tracking for Derivative-free Distributed Optimization

Mimmo,

Carnevale,

Testa

et al. 2021

Preprint

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In this paper, we deal with a network of agents that want to cooperatively minimize the sum of local cost functions depending on a common decision variable. We consider the challenging scenario in which objective functions are unknown and agents have only access to local measurements of their local function. We propose a novel distributed algorithm which combines a gradient tracking policy with an extremum seeking technique to estimate the global descent direction. The joint use of these two techniques results in a distributed optimization scheme which is proven to provide arbitrarily accurate solution estimate through the combination of Lyapunov and averaging analysis approaches with consensus theory. To corroborate the theoretical results, we perform numerical simulations in a personalized optimization framework and a cooperative robotics scenario.

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Cooperative Extremum Seeking Control via Sliding Mode for Distributed Optimization

Cited by 2 publications

References 23 publications

Distributed Extremum-Seeking for Wind Farm Power Maximization Using Sliding Mode Control

Distributed Extremum-Seeking for Wind Farm Power Maximization Using Sliding Mode Control

Extremum Seeking Tracking for Derivative-free Distributed Optimization

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