Fuzzy logic in active structural control

Casciati, Fabio; Faravelli, Lucia; Yao, Timothy

doi:10.1109/ijcf.1994.375123

Cited by 4 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A straightforward application is the one covering a linear, single-degree-of-freedom (SDOF) system subjected to (simulated) seismic activity [14,16]. Let the loading be a seismic-like simulated time history record consisting of a Kanai-Tajimi filtered white noise [15] without time modulation.…”

Section: A Linear System Implementationmentioning

confidence: 99%

Control of nonlinear structures using the fuzzy control approach

1996

Self Cite

View full text Add to dashboard Cite

Active control of structures against environmental loads, such as earthquakes and wind, has received much attention in the last decade. Much of this research has involved development of control algorithms based on the assumption of exact knowledge of the system parameters, a condition that does not always exist in real-life applications. In addition, most of this research effort has focused upon linear control. Few active control strategies can effectively handle control of nonlinear behavior.Fuzzy logic control provides a promising opportunity for this purpose. This paper discusses the motivations for using a Fuzzy Logic Controller (FLC) and the guidelines for its design. A brief description of a FLC is first outlined to introduce the appropriate definitions. The application of fuzzy control to nonlinear structures is then illustrated. Some numerical examples are presented to study the controlled response of a hysteretic plane frame under earthquake loading.

show abstract

Section: A Linear System Implementationmentioning

confidence: 99%

Control of nonlinear structures using the fuzzy control approach

1996

Self Cite

View full text Add to dashboard Cite

show abstract

“…Second, it is capable of incorporating several qualitative aspects of the human knowledge in the control laws [10,11,12,13]. Fuzzy control is based on the fuzzy set theory which allows for the qualitative, imprecise and/or vague information to be quantitatively included in the evaluation of a representative control action [5,6,7,10,11,12,13]. Such inherent uncertainty would probably be ignored in a conventional mathematical algorithm, thus, rendering inaccurate control forces.…”

Section: Fuzzy Controllers As Processorsmentioning

confidence: 99%

“…This degree of membership is the major difference between this approach and conventional mathematical methods. Fuzzy control comprises four main components [5,6,7,10,11,12,13];  Fuzzification: the state variables to be monitored, when measured, have crisp values. These values should be fuzzified, using fuzzy linguistic terms defined by the membership functions of the individual fuzzy sets.…”

Section: Fuzzy Controllers As Processorsmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Controllers: A Reliable Component of Smart Sustainable Structural Systems

Hassan¹

2012

Fuzzy Controllers- Recent Advances in Theory and Applications

View full text Add to dashboard Cite

Self-learning fuzzy control of civil structures

Faravelli

Yao²

Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuz

Self Cite

View full text Add to dashboard Cite

The application of ANFIS (Adaptive Network-based Fuzzy Inference System) to the fuzzy control of structures was investigated by the authors in a earlier paper. Through neural-network learning, ANFIS can be trained to replace an existing fuzzy controller. The resulting controller makes use of the more efficient Takagi-Sugeno inference scheme instead of COG (center of gravity)and is inheren.tly computationally faster.The next logical step accomplished in this paper is to implement the trajectory adaptive networks (TAN) and stage adaptive networks (SAN) that were proposed to be used with temporal back propagation to achieve a self-learning fuzzy controller. This approach should result in a fuzzy controller that is optimized to handle loads of the type used in the self-learning training. Because the learning process is goal-directed (i.e., a zero vector is the desired displacement behavior,), some optiin iza t ion is introduced.

show abstract

Fuzzy logic in active structural control

Cited by 4 publications

References 7 publications

Control of nonlinear structures using the fuzzy control approach

Control of nonlinear structures using the fuzzy control approach

Fuzzy Controllers: A Reliable Component of Smart Sustainable Structural Systems

Self-learning fuzzy control of civil structures

Contact Info

Product

Resources

About