Neural networks for structural control of a benchmark problem, active tendon system

✍ Scribed by Bani-Hani, Khaldoon; Ghaboussi, Jamshid

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 391 KB
• Volume: 27
• Category: Article
• ISSN: 0098-8847
• DOI: 10.1002/(sici)1096-9845(1998110)27:11<1225::aid-eqe780>3.0.co;2-t

✦ Synopsis

Methodology for active structural control using neural networks has been proposed by Ghaboussi and his co-workers [1][2][3][4][5][6][7][8] in the past several years. The control algorithm in the mathematically formulated methods is replaced by a neural network controller (neuro-controller). Neuro-controllers have been developed and applied in linear and nonlinear structural control. Neuro-controllers are trained with the aid of the emulator neural networks. The emulator neural network is trained to learn the transfer function between the actuator signal and the sensor reading and it uses that past values of these quantities to predict the future values of the sensor readings. In this paper, we apply the previously developed neuro-control method in the benchmark problem of the active tendon system. The emulator neural network is developed and trained using the evaluation model given in the benchmark problem which is considered to be the true representation of the active tendon system. However, a reduced-order model has been developed and used, along with the emulator neural network, to train the neuro-controller. The evaluation model represents the three story steel frame structure, including the actuator dynamics. The absolute acceleration of the first floor and the actuator piston displacement are used as feedback. Three neuro-controllers, with different control criteria, have been developed and their performances have been evaluated with the prescribed performances indexes. The robustness of the neuro-controllers in the presence of some severe uncertainties, has also been evaluated.