Control of Limit Cycle Oscillation in a Three Degrees of Freedom Airfoil Section Using Fuzzy Takagi-Sugeno Modeling

Bueno, Douglas Domingues; Góes, Luiz Carlos Sandoval; Gonçalves, Paulo José Paupitz

doi:10.1155/2014/597827

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…The input can be adapted as [ 46 ]: where γ 2 , γ 3 ≠ 0 are constants that are applied to tune its intensity. Therefore, during the time integration the controlling effect is given by: …”

Section: Methodsmentioning

confidence: 99%

Epileptic seizure suppression: A computational approach for identification and control using real data

Brogin,

Faber,

Reyes-Garcia

et al. 2024

PLoS ONE

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Epilepsy affects millions of people worldwide every year and remains an open subject for research. Current development on this field has focused on obtaining computational models to better understand its triggering mechanisms, attain realistic descriptions and study seizure suppression. Controllers have been successfully applied to mitigate epileptiform activity in dynamic models written in state-space notation, whose applicability is, however, restricted to signatures that are accurately described by them. Alternatively, autoregressive modeling (AR), a typical data-driven tool related to system identification (SI), can be directly applied to signals to generate more realistic models, and since it is inherently convertible into state-space representation, it can thus be used for the artificial reconstruction and attenuation of seizures as well. Considering this, the first objective of this work is to propose an SI approach using AR models to describe real epileptiform activity. The second objective is to provide a strategy for reconstructing and mitigating such activity artificially, considering non-hybrid and hybrid controllers − designed from ictal and interictal events, respectively. The results show that AR models of relatively low order represent epileptiform activities fairly well and both controllers are effective in attenuating the undesired activity while simultaneously driving the signal to an interictal condition. These findings may lead to customized models based on each signal, brain region or patient, from which it is possible to better define shape, frequency and duration of external stimuli that are necessary to attenuate seizures.

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Section: Methodsmentioning

confidence: 99%

Epileptic seizure suppression: A computational approach for identification and control using real data

Brogin,

Faber,

Reyes-Garcia

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The LMIs have been used to solve mechanical and aeromechanical problems—some of these are presented in Johnson and Erkus (2005, 2006), Bueno et al. (2014), Mura and Lovera (2015), Koroishi et al.…”

Section: Introductionmentioning

confidence: 99%

On the controllers' design to stabilize ground resonance helicopter

Silva

Bueno

Abreu

2019

Journal of Vibration and Control

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Ground resonance (GR) in helicopters is a potentially catastrophic instability commonly caused by coalescence of the regressive cyclic blade lag mode with the fuselage motion in certain rotor speed ranges. It can limit the helicopter operational envelope and the design of this type of vehicle can become a difficult task. Although a broad class of actuators allows the use of active and semi-active techniques to design feedback-based control systems, a limited number of works in the literature introduce formulations to compute the controller gain to suppress this phenomenon. Also, commonly, a control approach defines a feedback, particularly to a specific rotor speed. In this context, this work introduces an alternative methodology to design an active control system to stabilize GR of a helicopter. The proposed approach can suppress this instability in all rotor speed ranges by using only one control gain. Two strategies are proposed based on linear matrix inequalities (LMIs). The Lyapunov stability criteria are used and the unstable rotor speed is considered as an uncertain parameter to define an associated convex space. Using convex optimization, a robust control gain is computed until all the unstable rotor speed range is stabilized. Numerical simulations are carried out to demonstrate the effectiveness of this methodology. The results confirm the viability of the proposed approach to design active and semi-active controllers.

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“…Bueno et al (2014a) considers the analysis of stability in a range of velocity and altitude, instead of discrete points in the flight envelope. Bueno et al (2014b) also use the state space representation of aeroelastic system to design a controller for typical section airfoil with nonlinear freeplay. In the work of Bueno et al (2013), the authors use the concept of Gramian Matrix to study the stability of an airfoil, they claim that the procedure can help reducing the computational time for analysis of many study cases.…”

Section: Introductionmentioning

confidence: 99%

A reduced order state space model for aeroelastic analysis in time domain

Marqui

Bueno

Góes

et al. 2017

Journal of Fluids and Structures

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The objective of this paper is to describe a new method for modeling aeroelastic system in time domain based on a modification of the Laguerre Polynomials to represent complex quantities. These polynomials are used to approximate the unsteady aerodynamics forces which are defined in the frequency domain using the Doublet Lattice Method (DLM). In this approach, the size of the matrices representing the aeroelastic system remains the same as the matrices representing the structural dynamics behavior. It is an important point since classical state space aeroelastic models include lag states increasing the size of the matrices used to represent the system. The applicability of the method is demonstrated by numerical simulation performed on the benchmark wing structure. The approach offers promise mainly for complex systems such as real aircraft. Recent works involving the use of control theory for aeroelasticity stability analysis, include the work of Silva et al. (2006) which an aeroservoelastic plant for HILDA (High Lift-to-Drag Active) wing was identified using experimental data. The impulse response (IR) method was applied in order to identify the state-space model and the generalized predictive control (GPC) method was used to

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Control of Limit Cycle Oscillation in a Three Degrees of Freedom Airfoil Section Using Fuzzy Takagi-Sugeno Modeling

Cited by 10 publications

References 26 publications

Epileptic seizure suppression: A computational approach for identification and control using real data

Epileptic seizure suppression: A computational approach for identification and control using real data

On the controllers' design to stabilize ground resonance helicopter

A reduced order state space model for aeroelastic analysis in time domain

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