高速铁路外文资料54

Received 16 September 2006; received in revised form 9 April 2007; accepted 10 April 2007
Abstract Validation of fuzzy logic controllers that are optimized by a genetic algorithm is pursued in this study. Fuzzy logic controllers are designed to manage two 20 kN magnetorheological dampers for mitigation of seismic loads applied to a 9 m tall, three-story steel frame benchmark building. In order to develop a set of robust controllers that are sensitive to a variety of excitations, a genetic algorithm that considers multiple objectives concurrently is proposed. Four optimization objectives have been selected which necessitates employment of a controlled elitist genetic algorithm. Optimal controllers are identified and validated through numerical simulation and full-scale experimental shake table tests for a variety of seismic excitations. Furthermore, a modified version of the same genetic algorithm is used to identify a state-space representation of the benchmark structure. Results show that optimized fuzzy logic controllers are robust and effective in reduction of both displacement and acceleration responses for both near- and far-field seismic events. c 2007 Elsevier Ltd. All rights reserved.
Keywords: Experimental testing; Magnetorheological damper; Multi-objective genetic algorithm; Fuzzy logic control; System identification
1. Introduction Designers of tall buildings are facing an increasing demand for safety in high seismic areas. To alleviate these concerns engineers have employed a variety of techniques from use of increasingly stronger structural systems to the installation of an assortment of energy absorbing devices [1]. One such control mechanism that provides excellent promise to structural engineers is the magnetorheological (MR) damper. This semi-active control device offers significant control potential and high reliability for structural control applications [2]. MR dampers have been developed and used by numerous researchers as a semi-active alternative to passive and active control [3–15]. Primary benefits accrue from dynamic stability (i.e. semi-active resistance) and low power requirements of the MR damper. Large-scale numerical efforts have recently been conducted in reference to a base isolation benchmark problem [16] that involves MR dampers and fuzzy logic control [17].
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GA-optimized fuzzy logic control of a large-scale building for seismic loads
Please cite this article in press as: Shook DA, et al. GA-optimized fuzzy logic control of a large-scale building for seismic loads. Engineering Structures (2007), doi:10.1016/j.engstruct.2007.04.008
David A. Shook a , Paul N. Roschke a,∗ , Pei-Yang Lin b , Chin-Hsiung Loh c
a Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, USA b National Center for Research on Earthquake Engineering, Taipei, Taiwan, ROC c National Taiwan University, Department of Civil Engineering, Taipei, 106, Taiwan, ROC
Numerous control strategies, including fuzzy logic control, were investigated for the benchmark problem. Researchers employed expert knowledge to generate a fuzzy logic controller for operation of MR dampers. Researchers determined that fuzzy logic controlled MR dampers provide reliable performance with respect to all investigated seismic records. Efforts to experimentally investigate the effectiveness of structural systems that incorporate MR damper technology have been primarily limited to small-scale structures. However, one recent example of a large-scale test utilized a decentralized control strategy to mitigate the seismic response of a four degree of freedom steel frame building [3]. Four MR dampers were attached to the structure using a bracing configuration that spanned two floors. Results of the test program show that MR dampers can be very effective in reducing both displacement and acceleration response of the structure to seismic excitations. Another large-scale test included a 24,000 kg single degree of freedom structure that represents a hybrid base isolation system [9]. Here a friction pendulum system augmented by a 20 kN MR damper is employed to effectively mitigate the response of the structure to a suite of scaled earthquakes. Similarly, in another study a 40 kN MR damper was combined
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2 D.A. Shook et al. / Engineering Structures ( ) – Table 1 Benchmark building properties Parameter Floor height Floor dimensions Floor mass 1st floor 2nd floor 3rd floor Value 3m 3m×2m 5800 kg 5800 kg 6840 kg
Fig. 1. Benchmark structure on shake table.
with linear roller bearings as a base isolation system for a three degree of freedom structure. Test results also show favorable reductions in the response of the structure to seismic events [13]. Many researchers have used widely known control strategies such as Lyapunov, linear quadratic regulators, and other energybased methods for semi-active control of MR dampers [3, 4,6]. These controllers have been shown to be effective in numerical and physical testing. Each of these strategies relies on prior determination of structural characteristics such as mass and stiffness for controller formulation. When highly accurate models of the structure are available, they provide adequate to good control. For cases in which dynamic properties of the structure are not well known, design of a controller is decidedly more difficult. Nevertheless, recent efforts have accounted for uncertainties in H2 controllers and have been shown to be effective in spite of difficulties for active control applications [18]. Alternatively, fuzzy logic controllers are less sensitive to inaccurate prior determination of structural characteristics than traditional control methods [19]. The ability to compensate for uncertainties and non-linearities in numerical and physical applications gives fuzzy logic a key advantage over traditional control algorithms. Previous efforts towards optimization of fuzzy logic controllers have employed strategies such as neural networks and neuro-fuzzy algorithms [7]. These approaches were often limited to multiple-input, single-output (MISO) controllers. Other researchers have turned to genetic algorithms (GA) for development of multiple-input, multipleoutput (MIMO) fuzzy controllers that manage active control schemes [20]. They used a hybrid control system that incorporated an active mass damping system complimented by a set of actuators. While some endeavors have only focused on
∗ Corresponding author. Tel.: +1 979 845 1985.
E-mail address: p-roschke@ (P.N. Roschke). 0141-0296/$ - see front matter c 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.engstruct.2007.04.008