Use of Adaptive Networks in Fuzzy Control of Civil Structures

Faravelli, Lucia; Yao, Timothy

doi:10.1111/j.1467-8667.1996.tb00310.x

Cited by 75 publications

(28 citation statements)

References 7 publications

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“…Fuzzy logic controller has been investigated for the active control of civil engineering structures ͑Casciati et al 1996;Faravelli and Yao 1996;Subramanian et al 1996;Ayyub et al 1997;Battaini et al 1998b;Naghdy et al 1998;Al Dawod et al 1999a,b,c͒ and the current study builds on previous work in this area.…”

Section: Introductionmentioning

confidence: 75%

Active Control of Cross Wind Response of 76-Story Tall Building Using a Fuzzy Controller

et al. 2004

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This paper focuses on the benchmark problem application regarding the vibration control of tall buildings under cross wind excitation. The building under consideration is the 76-story, 306-m tall reinforced concrete office tower proposed for the city of Melbourne, Australia. The adopted control scheme consists of an active tuned mass damper ͑ATMD͒ where the control action is achieved by a fuzzy logic controller ͑FLC͒. The main advantage of the FLC is its inherent robustness and ability to handle any nonlinear behavior of the structure and the fact that its implementation does not require a mathematical model of the structure. This benchmark study is based on specified design constraints for the ATMD to be considered in the design of the proposed control scheme. The performance of the controller has been demonstrated through the uncertainty in stiffness ͑ϩ15 and Ϫ15% variation from initial stiffness͒ of the building. The results of the simulation show a good performance by the fuzzy controller for all cases tested. Also the results show that the fuzzy controller performance is similar to the linear quadratic Gaussian ͑LQG͒ controller, while possessing several advantages over the LQG controller.

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Section: Introductionmentioning

confidence: 75%

Active Control of Cross Wind Response of 76-Story Tall Building Using a Fuzzy Controller

et al. 2004

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“…The governing relations are equations (1) and (2). The fuzzy logic adopted is specified in Table III.…”

Section: Numerical Resultsmentioning

confidence: 99%

Fuzzy control of structural vibration. An active mass system driven by a fuzzy controller

Battaini

Casciati

Faravelli

1998

Earthquake Engng. Struct. Dyn.

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SUMMARYThe authors are engaged in a long-term research project studying the potential of fuzzy control strategies for active structural control in civil engineering applications. The advantage of this approach is its inherent robustness and its ability to handle the non-linear behaviour of the structure. Moreover, the computations for driving the controller are quite simple and can easily be implemented into a fuzzy chip. In this paper attention is focused on the response of a three-storey frame, subjected to earthquake excitation, controlled by an active mass driver located on the top floor. The design and the implementation of the controller driving the AMD system are discussed.

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“…This is because they are responsible for mapping the inputs and outputs to their respective universes of discourses. Adjustment of these factors may be achieved using heuristic approaches (Daugherity et al 1992;Driankov et al 1993;Faravelli and Yao 1996;Li and Gatland 1996), neuro-like approaches (Nishimori et al 1994;Chao and Teng 1997), genetic algorithms (Arslan and Kaya 2001;Zhao et al 2003), gain scheduling (Jang andGulley 1994;Zhao 2001) and selftuning (Maeda et al 1990;Woo et al 2000;Zhao 2001). The latter consists in using a fuzzy decision making system to vary the values of one or more of the scaling factors according to changes in the input variables to the fuzzy controller or the input excitation to the system.…”

Section: Introductionmentioning

confidence: 98%

Structural vibration reduction using self-tuning fuzzy control of magnetorheological dampers

Wilson

Abdullah

2010

Bull Earthquake Eng

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Among the control devices considered for dissipating seismic energy and reducing structural vibrations is the magnetorheological (MR) damper which consists of a hydraulic cylinder filled with a suspension of micron-sized, magnetically polarizable iron particles capable of reversibly changing from free-flowing, linear viscous fluid to semi-solid with the application of a magnetic field. Several algorithms have been proposed for regulating the amount of damping provided by MR dampers. An attractive option is the use of fuzzy controllers because they are simple, intrinsically robust, and they do not depend on a model of the system. Tuning of these controllers, however, has shown to be a difficult task because of the large number of parameters involved. This paper proposes a self-tuning fuzzy controller to regulate MR dampers' properties and reduce structural responses of single degree-of-freedom seismically excited structures. Robustness to changes in seismic motions and structural characteristics was assessed by subjecting a rigid and a flexible building to different earthquake records. Results show that the self-tuning controller proposed effectively reduced responses of both structures to all earthquakes considered. In addition, results were compared to those of a fuzzy controller with constant scaling factors and to those of two passive strategies: "passive on" and "passive off", where the current to the MR dampers was set to its maximum allowable value, and zero, respectively.

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Use of Adaptive Networks in Fuzzy Control of Civil Structures

Cited by 75 publications

References 7 publications

Active Control of Cross Wind Response of 76-Story Tall Building Using a Fuzzy Controller

Active Control of Cross Wind Response of 76-Story Tall Building Using a Fuzzy Controller

Fuzzy control of structural vibration. An active mass system driven by a fuzzy controller

Structural vibration reduction using self-tuning fuzzy control of magnetorheological dampers

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