Research on Intelligent Fuzzy Optimal Active and Hybrid Control Systems of Building Structures - Verification of Optimization Method on Switching Rules of Control Forces

Takagi, Kousuke; Tani, Akinori; Kawamura, Hiroshi

Recently, many reseraches on active control systems of building structures are preformed based on modern control theory and are installed real buildings. The authors have already proposed intelligent fuzzy optimal active control (IFOAC) systems. IFOAC systems imitate intelligent activities of human brains such as prediction, adaptation, decision-kaking and so on. In IFOAC systems, objective and subjective judgements on the active control can be taken into account. However, IFOAC systems are considered to be suitable for far-field erathquake and control effect becomes small in case of near-field earthqaukes which include a few velosity pules with large amplitudes. To improve control effect in case of near-souece earthquakes, the authors have also proposed hybrid control (HC) systems, in which IFOAC systems and fuzzy control system are combined. In HC systems, the fuzzy control systems are introduced as a reflective fuzzy active control (RFAC) system and imitates spinal reflection of human. In HC systems, active control forces are activated to buildings in accordance with switching rules on active control forces. In this paper, optimizations on fuzzy control rules in RFAC system and switching rules of active control forces in HC system are performed by Parameter-Free Genetic Algorithms (PfGAs). Here, the optimization is performed by using different earthquake inputs. The results of digital simulations show that the HC system can reduce maximal response displacements under restrictions on strokes of the actuator effectively in case of a near-source earthquake and the effectiveness of the proposed HC system is discussed and clarified.

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Takagi, Kousuke / Tani, Akinori / Kawamura, Hiroshi: Research on Intelligent Fuzzy Optimal Active and Hybrid Control Systems of Building Structures - Verification of Optimization Method on Switching Rules of Control Forces. 2004.

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