Paper
17 June 1998 Active control of the Nanjin TV transmission tower with lateral-torsional motion
Jong Cheng Wu, Jann N. Yang
Author Affiliations +
Abstract
A 310 m Nanjing TV transmission tower in China will be installed with an active mass driver on the upper observation deck in order to reduce the acceleration responses under strong winds. The wind-induced structural responses considered in this paper include the coupling effect of lateral and torsional motions. The along-wind and across-wind components of the wind velocity are modeled as random processes defined by the Davenport cross-power spectra. The structural responses, in particular the acceleration, increase due to the coupling effect of lateral and torsional motions. This paper presents the Linear Quadratic Gaussian (LQG) control strategy using accelerations as the feedback quantities to reduce the tower response. Emphasis is placed on the practical applications, such as the limitations on actuator peak force and stroke, limited number of sensors, noise pollution, etc. A state reduced-order system has been established to design the dynamic output feedback controllers. The power spectral density and rms of acceleration responses of the TV transmission tower equipped with an active mass driver have been computed. Simulation results demonstrate that the LQG strategy is remarkable in reducing the lateral and torsional motions of the tower and it is suitable for the full-scale implementation of active mass driver on Nanjing Tower.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jong Cheng Wu and Jann N. Yang "Active control of the Nanjin TV transmission tower with lateral-torsional motion", Proc. SPIE 3325, Smart Structures and Materials 1998: Smart Systems for Bridges, Structures, and Highways, (17 June 1998); https://doi.org/10.1117/12.310613
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Actuators

Control systems

Zirconium

Aerodynamics

Matrices

Sensors

Stochastic processes

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