On-line control of a flexible beam using adaptive fuzzy controller and piezoelectric actuators

Abreu, Gustavo; Ribeiro, José Francisco Ferreira

doi:10.1590/s0103-17592003000400005

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Much of the recent research work has focused on the development of suitable models for the active structures incorporating piezoelectric layers or the development of new actuating solutions. A second area of interest is the development of suitable control laws, with approaches ranging from the classical velocity-feedback or proportional derivative (PD) control to "postmodern" linear matrix inequality (LMI)-based control (da Silva et al, 2004), fuzzy logic (de Abreu and Ribeiro, 2003) or neural control (Jha and Rower, 2001). It is interesting to note that for this class of vibration reduction problems robust control synthesis approaches are not well represented.…”

Section: Introductionmentioning

confidence: 99%

H ∞ Control with μ-analysis of a Piezoelectric Actuated Plate

Iorga

Baruh²,

Ursu

2009

Journal of Vibration and Control

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This paper presents the development of a multimodal H∞controller for piezoelectric actuated plates designed to simultaneously suppress vibrational components of the first two modes. The controller is developed for a reduced structural model. The closed-loop control scheme is subject to both uncertainties due to control and observation spillover in the unmodeled residual modes and to parametric errors in the structural model. The closed-loop stability and performance robustness is analyzed using μ-analysis, and numerical investigations indicate that the controller tolerates uncertainties of significant size.

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Section: Introductionmentioning

confidence: 99%

H ∞ Control with μ-analysis of a Piezoelectric Actuated Plate

Iorga

Baruh²,

Ursu

2009

Journal of Vibration and Control

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“…It has been employing previously with the SCADA dataset in reference [40]. Figure 9 shows the basic configuration of the fuzzy logic system [81]. Fuzzy inference is the process of formulating the mapping of input data based on membership functions, fuzzy logic operators (and / or), and If-Then rules at an output using the Fuzzy logic [21].…”

Section: Validation With Fuzzy Logicmentioning

confidence: 99%

Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines

Marugán

Chacón

Márquez

2019

Reliability Engineering & System Safety

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Operations and maintenance tasks are critical to the reliability of a wind turbine. The state-of-the-art demonstrates the effectiveness of reliability centred maintenance, but there are no research studies that consider false alarms to reliability of the wind turbines. This paper presents a novel approach based on artificial neural networks to reliability centred maintenance. The methodology is employed for false alarm detection and prioritization, training the artificial neural networks over the time to increase the system reliability. The approach is applied to a real dataset from a supervisory control and data acquisition system together with a vibration monitoring system of a wind turbine. The results accuracy is done by confusion matrices, studding real alarms with the estimations provided by the approach, and the results are validated with real false alarms and compared by the results given by a fuzzy logic model. The method provides accuracy results (over 90%). A novelty is to use a two real dataset from a wind turbine to create a redundant response to detect false alarms by artificial neural networks.

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Fuzzy Logic Applied to SCADA Systems

Benmessaoud

Marugán

Mohammedi

et al. 2017

Proceedings of the Eleventh International Conference on Management Science and Engineering Management

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This article focuses on the monitoring of a wind farm in real time based on big data collected by Supervisory Control and Data Acquisition (SCADA) system. The decisionmaking of the type of maintenance to be applied can be insured by SCADA system. This system generates alarms based on the collected data. False alarms cause false interventions by the maintenance team resulting in loss of production and costs. The reduction of these false alarms makes it possible to contribute better to the management of the maintenance of the wind farm. In this paper, we propose a new approach for the identification of alarms by Fuzzy Logic based on the data collected by the SCADA system. The alarms generated in this case can be divided into two categories: orange alarms corresponding to faults requiring the intervention of preventive maintenance and red alarms corresponding to critical states that can cause system failures.

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On-line control of a flexible beam using adaptive fuzzy controller and piezoelectric actuators

Cited by 6 publications

References 10 publications

H ∞ Control with μ-analysis of a Piezoelectric Actuated Plate

H ∞ Control with μ-analysis of a Piezoelectric Actuated Plate

Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines

Fuzzy Logic Applied to SCADA Systems

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