Active neural predictive control of seismically isolated structures
Active neural predictive control of seismically isolated structures
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DOI：
10.1002/stc.2061
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发表时间：
2018-01
影响因子：
5.4
通讯作者：
H. Khodabandehlou;G. Pekcan;M. S. Fadali;M. Salem
中科院分区：
文献类型：
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作者：
H. Khodabandehlou;G. Pekcan;M. S. Fadali;M. Salem
提出了一种基于小波神经网络（WNN）和模型预测控制（MPC）的在线辨识控制方案。小波网络由一个带小波激活函数的反向传播神经网络和一个平行前馈项组成。利用小波神经网络对结构系统进行识别，利用模型对MPC进行预测。采用梯度下降算法对反向传播网络参数和控制器进行训练，使性能指标最小化。前馈分量使用递归最小二乘进行训练。发现后者可以大幅减少隐层神经元的数量，显著降低神经网络的计算负荷。由于控制器的一般结构，即使在固定学习率的严格条件下，其性能也令人满意。通过一系列采用传统铅橡胶支座的5层隔震结构的计算模拟，证明了控制的有效性。近场（脉冲）和远场地面运动的所有响应幅值都显著降低，包括变形减少以及相应的加速度响应降低。特别是，该控制器有效地调节了隔震级的视刚度。
An online identification and control scheme based on a wavelet neural network (WNN) and model predictive control (MPC) are presented. The WNN comprises a backpropagation neural network with wavelet activation functions and a parallel feedforward term. The WNN is used to identify the structural system, and the model is used to provide the predictions for MPC. The backpropagation network parameters and the controller are trained by the gradient descent algorithm to minimize performance indices. The feedforward component is trained using recursive least squares. The latter is found to drastically reduce the number of hidden layer neurons and significantly reduce the computational load of the neural network. Due to the general structure of the controller, its performance is satisfactory even under the strict condition imposed by a fixed learning rate. The efficacy of the control was demonstrated through a series of computational simulations of a 5‐story seismically isolated structure with conventional lead‐rubber bearings. Significant reductions of all response amplitudes were achieved for both near‐field (pulse) and far‐field ground motions, including reduced deformations along with corresponding reduction in acceleration response. In particular, the controller effectively regulated the apparent stiffness at the isolation level.