Bio-inspired iterative learning technique for more effective control of civil infrastructure
Bio-inspired iterative learning technique for more effective control of civil infrastructure
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仿生迭代学习技术可更有效地控制民用基础设施
DOI：
10.1117/12.2514334
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Fogg, Camille
中科院分区：
文献类型：
--
作者：
Peckens, Courtney;Fogg, Camille
土木结构，如建筑物和桥梁，由于地震或强风引起的广泛环境负荷，经常处于失效的危险之中。为了最大限度地降低这种风险，提出了控制系统在民用基础设施稳定中的应用。然而，包括通信延迟、驱动节点的计算淹没和数据丢失在内的实现挑战一直阻碍着大规模部署。为了克服这些挑战，可以从生物中枢神经系统使用的信号处理技术中获得灵感。这项工作使用了一个生物启发的无线传感器节点，能够实时频率分解，以简化执行节点的计算，从而减轻通信和计算淹没，实现实时控制。简化后的控制律变成了=𝐰𝐍，其中是要施加的控制力，𝐰是特定于结构的权重矩阵，𝐍是来自无线传感器节点的位移数据。没有经验解来推导最优权重矩阵𝐰，在本研究中使用粒子群优化技术作为确定该矩阵值的手段。为了得到最有效的控制，对该优化方法的多个参数进行了探索。将这种仿生方法应用于五层基准结构的模拟中，并使用性能指标得出结论，该方法与传统控制方法相似。
Civil structures, such as buildings and bridges, are constantly at risk of failure due to extensive environmental loads caused by earthquakes or strong winds. In order to minimize this risk, the application of control systems for civil infrastructure stabilization has been proposed. However, implementation challenges including communication latencies, computation inundation at the actuation node, and data loss have been impeding large-scale deployment. In order to overcome many of these challenges, inspiration can be drawn from the signal processing techniques employed by the biological central nervous system. This work uses a bio-inspired wireless sensor node, capable of real-time frequency decomposition, to simplify computations at an actuating node, thus alleviating both communication and computation inundation and enabling real-time control. The simplistic control law becomes 𝐅 = 𝐰𝐍, where 𝐅 is the control force to be applied, 𝐰 is a weighting matrix that is specific to the structure, and 𝐍 is the displacement data from the wireless sensor node. There is no empirical solution for deriving the optimal weighting matrix, 𝐰, and in this study the particle swarm optimization technique was used as a means for determining values for this matrix. Multiple parameters of this optimization method were explored in order to produce the most effective control. This bio-inspired approach was applied in simulation to a five story benchmark structure and using performance metrics it was concluded that this method performed similar to more traditional control method.
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