Active control of SDF systems using artificial neural networks.
A study of the application of artificial neural networks (ANNs) to active structural control is presented. A simple effective strategy for the on-line control of single-degree-of-freedom (SDF) structures is proposed. The strategy is to apply the control force at every time step to destroy the buildup of the system response, and the control force needed for the next time step is fully determined from the information available at the current time ; therefore the time delay associated with the control algorithm is eliminated. The control algorithm can be implemented for either a closed or open-closed loop controller. The controller uses a trained ANN to determine the control force such that the velocity induced at the preceding time step is canceled. A feedforward neural network with an adaptive backpropagation training method is used in this study. In the backpropagation training, the learning rate is determined by ensuring the decrease of the error function of the input-output training patterns at each training cycle. Numerical examples of SDF systems under earthquake excitations are given to illustrate the effectiveness of the proposed control strategy. The uncertainties in the time history of the excitation and in the modeling of the system, including the magnitudes of the excitations, the natural frequency and nonlinearity of the systems are examined. Significant reduction of the response is observed. Also, is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history, an ability that linear control laws lack.
- Research Organization:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- DOE Contract Number:
- DE-AC02-06CH11357
- OSTI ID:
- 938217
- Report Number(s):
- ANL/RE/JA-28291; CMSTCJ; TRN: US200908%%130
- Journal Information:
- Comput. Struct., Journal Issue: 1997; ISSN 0045-7949
- Country of Publication:
- United States
- Language:
- ENGLISH
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