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Robust semi-active control for uncertain structures and smart dampers

机译：不确定结构和智能阻尼器的鲁棒半主动控制

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摘要

Recent developments in semi-active control technology have led to its application in civil infrastructures as an efficient strategy to protect susceptible structures against seismic and wind induced vibration. The reliable and robust performance of semi-active systems depends on the level of uncertainties in the structural parameters as well as on the sensors’ measurement and on smart mechanical dampers. A common source of uncertainties in semi-active control devices is related to the inherent nonlinear nature of these devices, thermal variation, or their malfunctioning. This study deals with the robust H_∞ control problem and aims to model different sources of uncertainty. The uncertainty of the structural model and damper force are assumed to be norm bounded random variables. By using linear fractional transformation (LFT), the uncertain part of the system is decoupled from the nominal parameters of the system. The robust H_∞ controller is designed to achieve consistent performance in structures including nominal and perturbed dynamics. Additionally, to reduce the uncertainty of the damper force, an inverse model of the magnetorheological (MR) damper is developed based on an adaptive neurofuzzy inference system (ANFIS). The robustness of the proposed algorithm is validated by numerical simulations.
机译：半主动控制技术的最新发展已使其在民用基础设施中的应用成为一种有效的策略，可以保护敏感结构免受地震和风引起的振动。半主动系统的可靠和强大性能取决于结构参数的不确定性水平以及传感器的测量值和智能机械阻尼器。半主动控制设备中常见的不确定性来源与这些设备的固有非线性特性，热变化或其故障有关。这项研究解决了鲁棒的H_∞控制问题，旨在对不确定性的不同来源进行建模。结构模型和阻尼力的不确定性被认为是有界随机变量。通过使用线性分数变换（LFT），系统的不确定部分与系统的标称参数解耦。健壮的H_∞控制器旨在在包括标称和扰动动力学在内的结构中实现一致的性能。另外，为了减少阻尼力的不确定性，基于自适应神经模糊推理系统（ANFIS）开发了磁流变（MR）阻尼器的逆模型。通过数值仿真验证了所提算法的鲁棒性。

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