Identification-Based Closed-Loop Control Strategies for a Cylinder Wake Flow

Atam, Ercan; Mathelin, Lionel; Cordier, Laurent

doi:10.1109/tcst.2016.2604779

Cited by 7 publications

(8 citation statements)

References 20 publications

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“…Atam et al. (2016) used a suction actuation steered by four different controllers and manage to match a target drag efficiently. The work of Arakeri & Shukla (2013), who impose a tangential velocity on the cylinder surface and force a quasi-upstream–downstream symmetric flow, is not included in the comparison for similar reasons.…”

Section: Resultsmentioning

confidence: 99%

“…This issue is tackled in the study of Atam et al. (2016), where gain-scheduled controllers are evaluated on a cylinder flow with time-varying Reynolds number. Unlike Tang et al.…”

Section: Resultsmentioning

confidence: 99%

“…Fujisawa, Kawaji & Ikemoto (2001) and Siegel, Cohen & McLaughlin (2003) used variable phase and proportional and differential control to reduce the drag of low-Reynolds-number cylinder flows. Atam, Mathelin & Cordier (2016) used ARMAX/ARX models with gain scheduling to reach a target drag value in varying-Reynolds-number conditions. Several studies implemented robust or control methods based on resolvent analysis (Jin, Illingworth & Sandberg 2019).…”

Section: Introductionmentioning

confidence: 99%

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Robust flow control and optimal sensor placement using deep reinforcement learning

2021

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Section: Resultsmentioning

confidence: 99%

“…This issue is tackled in the study of Atam et al. (2016), where gain-scheduled controllers are evaluated on a cylinder flow with time-varying Reynolds number. Unlike Tang et al.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust flow control and optimal sensor placement using deep reinforcement learning

2021

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“…Proof: Immediately, after combining the formula (7) and Theorem (1). Revised expression (14) in form of…”

Section: A Towards a General Theory Of Minimum-energy Perfect Contromentioning

confidence: 99%

A New Geometric-Oriented Minimum-Energy Perfect Control Design in the IMC-Based State-Space Domain

Hunek

Feliks

2020

IEEE Access

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A new geometric approach providing the minimum-energy issue for inverse model controlrelated perfect regulation of linear time-invariant multi-input/single-output plants described in the discretetime state-space framework is proposed in the paper. Recent results have shown that the minimum-norm T-inverse does not guarantee the minimum-energy perfect control design, which has been confirmed by heuristic studies only. The new proposal, postulated throughout the manuscript, certifies the potential of nonunique σ-inverse regarding the minimum-energy behavior of inverse model control-based structures. After application of the proposed geometric approach dedicated to some class of state-space systems, we can precisely calculate the total energy of the multivariable perfect control runs. Thus, the analytical new methodology allows to obtain the minimum-energy inverse model control schemes, what constitutes the main accomplishment of the paper. Additionally, the aim of future analytical exploration covering the entire class of right-invertible state-space systems is clearly focused. INDEX TERMS Geometric solution, perfect control, minimum-energy problem, inverses of nonsquare matrices, discrete-time state-space domain, LTI MIMO.

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“…One can prove that the smoothed data lead to a less complex identification model than the raw data. The identification models belong here to ARMAX class, which can be found in a number of identification and control applications (e.g., [ 12 , 13 , 15 , 16 , 17 ]).…”

Section: Introductionmentioning

confidence: 99%

Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data

Ștefănoiu

Culita

2020

Sensors

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This article introduces the spline approximation concept, in the context of system identification, aiming to obtain useful autoregressive models of reduced order. Models with a small number of poles are extremely useful in real time control applications, since the corresponding regulators are easier to design and implement. The main goal here is to compare the identification models complexity when using two types of experimental data: raw (affected by noises mainly produced by sensors) and smoothed. The smoothing of raw data is performed through a least squares optimal stochastic cubic spline model. The consecutive data points necessary to build each polynomial of spline model are adaptively selected, depending on the raw data behavior. In order to estimate the best identification model (of ARMAX class), two optimization strategies are considered: a two-step one (which provides first an optimal useful model and then an optimal noise model) and a global one (which builds the optimal useful and noise models at once). The criteria to optimize rely on the signal‑to‑noise ratio, estimated both for identification and validation data. Since the optimization criteria usually are irregular in nature, a metaheuristic (namely the advanced hill climbing algorithm) is employed to search for the model optimal structure. The case study described in the end of the article is concerned with a real plant with nonlinear behavior, which provides noisy acquired data. The simulation results prove that, when using smoothed data, the optimal useful models have significantly less poles than when using raw data, which justifies building cubic spline approximation models prior to autoregressive identification.

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Identification-Based Closed-Loop Control Strategies for a Cylinder Wake Flow

Cited by 7 publications

References 20 publications

Robust flow control and optimal sensor placement using deep reinforcement learning

Robust flow control and optimal sensor placement using deep reinforcement learning

A New Geometric-Oriented Minimum-Energy Perfect Control Design in the IMC-Based State-Space Domain

Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data

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