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A stochastic averaging-based optimal control method for nonlinear systems: Application to a building with soil-structure interactions

机译:基于随机平均的非线性系统最优控制方法:在具有土-结构相互作用的建筑物中的应用

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This paper presents a nonlinear stochastic optimal control algorithm based on an enhanced stochastic averaging method of energy envelope to mitigate the response of nonlinear hysteretic systems. The enhanced stochastic averaging strategy for non-integrable hysteretic systems addresses some of the shortcomings of conventional methods including consideration of a collective contribution of damping terms and the assumption of the independence of stochastic excitations. Hamilton-Jacobi-Bellman equation is used to formulate the optimization problem with a cost function that incorporates high order terms of the energy of the system in order to optimize the dynamic response. Unlike other techniques, this strategy considers the stochastic nonlinear behavior of the system in the optimization process of the control design. The performance of the proposed controller is evaluated for a one-story building on a nonlinear foundation under white noise and historic ground motions. The results are compared with those for the uncontrolled structure and the structure controlled using a linear quadratic regulator algorithm designed using stochastic linearization (SLQR). The proposed method is shown to provide considerable improvements over other control cases. In particular under Kobe ground motion, 14% and 21% reductions are observed for the maximum relative displacement and foundation displacement, respectively compared to the SLQR with similar maximum control force levels. (C) 2016 Elsevier Ltd. All rights reserved.
机译:提出了一种基于能量包络增强的随机平均方法的非线性随机最优控制算法,以减轻非线性滞后系统的响应。不可积分磁滞系统的增强型随机平均策略解决了常规方法的一些缺点,包括考虑阻尼项的集体贡献以及假设随机激励的独立性。 Hamilton-Jacobi-Bellman方程用于用成本函数表述优化问题,该函数包含系统能量的高阶项,以优化动态响应。与其他技术不同,此策略在控制设计的优化过程中考虑了系统的随机非线性行为。在白噪声和历史地面运动的情况下,针对非线性基础上的一层建筑物评估了所提出控制器的性能。将结果与非受控结构的结果和使用随机二次线性化(SLQR)设计的线性二次调节器算法控制的结果进行比较。所提出的方法显示出与其他控制情况相比有相当大的改进。特别是在神户地面运动下,与具有相似最大控制力水平的SLQR相比,最大相对位移和基础位移分别降低了14%和21%。 (C)2016 Elsevier Ltd.保留所有权利。

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