Iterative Feedback Tuning of the Proportional-Integral-Differential Control of Flow Over a Circular Cylinder

Son, Donggun; Choi, Haecheon

doi:10.1109/tcst.2018.2828381

Cited by 19 publications

(4 citation statements)

References 30 publications

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“…The transverse velocity in the near-wake of the cylinder is a useful information indicating the status of the Kármán vortex shedding. Thus, various feedback controls applied to flow over a circular cylinder measure the transverse velocity in the wake as sensors and aim to reduce its amplitude to weaken the strength of the Kármán vortex shedding [27][28][29][30][31]. However, it would not be practical to measure the transverse velocity in the wake in real applications of these control methods.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we aim to predict the near-wake transverse velocity in laminar flow over a circular cylinder by using a neural network with instantaneous wall pressures on the cylinder surface as the input variables. The transverse velocity in the wake of a circular cylinder has been considered a good indicator for the state of Kármán vortex shedding [27][28][29][30][31]. In this regard, one of the motivations for constructing neural networks to predict the transverse velocity is to further integrate them, in future studies, with active feedback control methods such as proportional-integral-derivative (PID) control [27,29], whose control purpose is to mitigate the strength of the Kármán vortex shedding.…”

Section: Datasetmentioning

confidence: 99%

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Prediction of Near-Wake Velocity in Laminar Flow over a Circular Cylinder Using Neural Networks with Instantaneous Wall Pressure Input

Yun

Lee

2023

Applied Sciences

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In the present study, to predict the transverse velocity field in the near-wake of laminar flow over a circular cylinder at the Reynolds numbers of 60 and 300, we construct neural networks with instantaneous wall pressures on the cylinder surface as the input variables. For the two-dimensional unsteady flow at Re=60, a fully connected neural network (FCNN) is considered. On the other hand, for a three-dimensional unsteady flow at Re=300 having spanwise variations, we employ two different convolutional neural networks based on an encoder–FCNN (CNN-F) or an encoder–decoder (CNN-D) structure. Numerical simulations are carried out for both Reynolds numbers to obtain instantaneous flow fields, from which the input and output datasets are generated for training these neural networks. At the Reynolds numbers considered, the neural networks constructed accurately predict the transverse velocity fields in the near-wake over the cylinder using the information of instantaneous wall pressures as the input variables. In addition, at Re=300, it is observed that CNN-D shows a better prediction ability than CNN-F.

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Section: Discussionmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Prediction of Near-Wake Velocity in Laminar Flow over a Circular Cylinder Using Neural Networks with Instantaneous Wall Pressure Input

Yun

Lee

2023

Applied Sciences

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“…In this study, many control methods are proposed to achieve good performance for noninteger-order systems. In addition, the researchers are also working to develop various control methods, such as sliding mode control [16][17][18], pinning control [19,20], adaptive fuzzy control [21,22], PID control [23,24], and backstepping control [25,26], to achieve the effective control. For example, an adaptive sliding mode control for FOS stabilization was studied in [27].…”

Section: Introductionmentioning

confidence: 99%

T-S Fuzzy Control of Uncertain Fractional-Order Systems with Time Delay

Hao

Zhang

2021

Journal of Mathematics

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In this article, the adaptive control of uncertain fractional-order time-delay systems (FOTDSs) with external disturbances is discussed. A Takagi-Sugenu (T-S) fuzzy model with if-then rules is adopted to characterize the dynamic equation of the FOTDS. Besides, a fuzzy adaptive method is proposed to stabilize the model. By utilizing the Lyapunov functions, a robust controller is constructed to stabilize the FOTDS. Due to the uncertainty of system parameters, some fractional-order adaptation laws are designed to update these parameters. At the same time, some if-then rules with linear structure based on the fuzzy T-S adaption concept are established. The designed method not only guarantees that the state of closed-loop system asymptotically converges to origin but also keeps the signal in the FOTDS bounded. Finally, the applicability of the control method is proved by simulation examples.

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“…The estimated gradient and Hessian are subsequently used in the Gauss-Newton optimization procedure to iteratively obtain the optimal controller parameters. This IFT approach has been widely used in many applications such as path-tracking control of industrial robots [17], [31], ultra-precision wafer stage [32], [33], flow control over a circular cylinder [34] and compliant rehabilitation robots [18], etc. Extensions of the IFT idea to other types of controller includes iterative dynamic decoupling control [35], disturbance observer sensitivity shaping [36], iterative feedforward tuning [37], [38], and 3-DOF controller tuning [39], [40] etc.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Multiobjective Controller Optimization for a Magnetically Levitated Nanopositioning System

Zhu

et al. 2020

IEEE/ASME Trans. Mechatron.

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The performance achieved with traditional modelbased control system design approaches typically relies heavily upon accurate modeling of the motion dynamics. However, modeling the true dynamics of present-day increasingly complex systems can be an extremely challenging task; and the usually necessary practical approximations often renders the automation system to operate in a non-optimal condition. This problem can be greatly aggravated in the case of a multi-axis magneticallylevitated (maglev) nanopositioning system where the fully floating behavior and multi-axis coupling make extremely accurate identification of the motion dynamics largely impossible. On the other hand, in many related industrial automation applications, e.g., the scanning process with the maglev system, repetitive motions are involved which could generate a large amount of motion data under non-optimal conditions. These motion data essentially contain rich information; therefore, the possibility exists to develop an intelligent automation system to learn from these motion data, and to drive the system to operate towards optimality in a data-driven manner. Along this line then, this paper proposes a data-driven model-free controller optimization approach that learns from the past non-optimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multi-objective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data; the multiobjective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. In the work here, experiments are then conducted on the maglev nanopositioning system to demonstrate the effectiveness of the proposed method, and the results show rather clearly the practical appeal of our methodology for related complex robotic systems with no accurate model available.

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Iterative Feedback Tuning of the Proportional-Integral-Differential Control of Flow Over a Circular Cylinder

Cited by 19 publications

References 30 publications

Prediction of Near-Wake Velocity in Laminar Flow over a Circular Cylinder Using Neural Networks with Instantaneous Wall Pressure Input

Prediction of Near-Wake Velocity in Laminar Flow over a Circular Cylinder Using Neural Networks with Instantaneous Wall Pressure Input

T-S Fuzzy Control of Uncertain Fractional-Order Systems with Time Delay

Data-Driven Multiobjective Controller Optimization for a Magnetically Levitated Nanopositioning System

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