Abstract This article presents the use of neural network predictive controller as a novel technique for vibration control of tall structures employing single degree of freedom active tuned mass damper (ATMD). Additionally, the proposed technique is compared with two modern control techniques: pole-placement controller and adaptive neuro-fuzzy inference controller. A scaled-down laboratory model is used to validate the control techniques. A linear and a nonlinear auto-regressive exogenous (ARX) models are identified for the constructed structure. A neural network predictive controller is designed using the nonlinear ARX model. Polynomial and state-space pole-placement controllers are designed using the linear ARX model. A fuzzy logic controller is designed for the structure and trained using adaptive neuro fuzzy inference system (ANFIS). Hardware-in-the-loop implementation of these controllers demonstrates that the neural network predictive controller combines the advantages of both pole-placement and the ANFIS controllers and reduces the settling time of the mass damper six times with same amplitude mitigation.

中文翻译：

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高层结构振动的神经网络预测控制：实验控制视觉

摘要 本文介绍了使用神经网络预测控制器作为采用单自由度主动调谐质量阻尼器 (ATMD) 的高层结构振动控制的新技术。此外，将所提出的技术与两种现代控制技术进行了比较：极点放置控制器和自适应神经模糊推理控制器。缩小的实验室模型用于验证控制技术。为构建的结构确定了线性和非线性自回归外生 (ARX) 模型。使用非线性 ARX 模型设计神经网络预测控制器。多项式和状态空间极点放置控制器是使用线性 ARX 模型设计的。模糊逻辑控制器是为该结构设计的，并使用自适应神经模糊推理系统 (ANFIS) 进行训练。