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Fault-tolerant adaptive control of nonlinear base-isolated buildings using EMRAN

机译：基于EMRAN的非线性隔震建筑的容错自适应控制。

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This paper presents a direct adaptive fault-tolerant neural control scheme for the active control of nonlinear hysteretic base-isolated buildings using the recently developed Extended Minimal Resource Allocation Network (EMRAN). EMRAN is a learning algorithm in which the structure of the neural controller is adapted on-line based on the input-output data. EMRAN starts with no hidden neurons and calculates the number of hidden neurons based on growing/pruning criteria. If the criteria are not met, then the parameters of the network are adjusted using an Extended Kalman Filter (EKF). The constants associated with the growing/pruning criteria and EKF are estimated using Genetic Algorithm (GA) optimization. The advantage of the proposed control architecture is its ability to learn on-line with no a priori training. Most of the existing studies in structural control using neural networks require computationally intensive off-line training. Consequently, once the network parameters are learnt, the parameters remain fixed. Such procedures require an accurate mathematical model of the system. These issues are addressed in the current controller scheme by utilizing the on-line adaptation capabilities of the neural networks. The advantages of on-line adaptation are demonstrated using the controller's capability to handle actuator failures and system uncertainties. Performance of the proposed control scheme is evaluated using the recently developed nonlinear three-dimensional base-isolated benchmark structure incorporating lateral-torsional superstructure behavior and the biaxial interaction of the nonlinear bearings in the isolation layer. Results show that the proposed controller scheme can achieve the desired performance objectives under both partial actuator failure conditions and large uncertainties associated with the system's parameters.
机译：本文提出了一种使用最新开发的扩展最小资源分配网络（EMRAN）的主动自适应容错神经控制方案，用于非线性滞后基础隔震建筑物的主动控制。 EMRAN是一种学习算法，其中神经控制器的结构根据输入输出数据进行在线调整。 EMRAN从没有隐藏神经元开始，然后根据增长/修剪标准计算隐藏神经元的数量。如果不符合标准，则使用扩展卡尔曼滤波器（EKF）调整网络参数。使用遗传算法（GA）优化估算与生长/修剪标准和EKF相关的常数。所提出的控制体系结构的优点是无需先验培训即可在线学习的能力。现有的大多数使用神经网络进行结构控制的研究都要求进行计算密集型的离线训练。因此，一旦学习了网络参数，参数就保持固定。这样的程序需要系统的精确数学模型。通过利用神经网络的在线适应能力，在当前的控制器方案中解决了这些问题。使用控制器处理执行器故障和系统不确定性的能力证明了在线自适应的优势。使用最近开发的非线性三维基础隔离基准结构，结合侧向扭转上部结构行为和隔离层中非线性轴承的双轴相互作用，评估了提出的控制方案的性能。结果表明，所提出的控制器方案在部分执行器故障条件下以及与系统参数相关的较大不确定性下都可以实现所需的性能目标。

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