A Data-driven Approach for the Design of Wide-Area Damping Controllers

Bernardo, Raul Torres; Campestrini, Lucíola; Oliveira, Ricardo C. L. F.; Aquino, Antonio F. C.; Dotta, Daniel

doi:10.1007/s40313-023-01026-0

Cited by 3 publications

(1 citation statement)

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“…Perhaps, this feature must be stressed here since it distinguishes ESC from other offline approaches of finding the extremum value of a known objective function with or without constraints (Tlili 2021). With the explosion of interest in learning algorithms (Harkat et al 2020), for all sorts of purposes including optimization and recently even for control, it would be regrettable if it were overlooked that ESC is also a learning-based approach (in the minimalistic, most efficient sense of learning), a model-free approach (Carvalho et al 2023), and also a data-based approach (Bernardo et al 2023). The real-time capabilities, as well as the rigorous convergence guarantees which ESC possesses, are what machine learning and reinforcement learning algorithm have yet to achieve.…”

Section: Discussionmentioning

confidence: 99%

Stochastic Multivariable Extremum Seeking Control Considering Input and Output Delays

Silva,

Pellanda,

Oliveira

2023

Preprint

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This paper introduces a design and analysis framework for a multivariable gradient-based stochastic extremum-seeking control system that considers delays. The study addresses multi-input systems with time delays in both input and output channels. The introduced new predictor feedback mechanism utilizes stochastic sinusoidal perturbation-based estimates of the Hessian's matrix in order to compensate the delay effects in the real-time optimizer. The incorporation of this feedback into the closed-loop system ensures exponential stability for the average dynamics and convergence to a small neighborhood around the unknown extremum point. The derived results are rigorously established through the application of backstepping transformation and stochastic averaging techniques in infinite dimensions. The effectiveness of the proposed predictor-based stochastic extremum seeking approach for delay compensation is illustrated through a numerical example.

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Section: Discussionmentioning

confidence: 99%