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Teaching Learning Based Optimization (TLBO) for Optimal Placement of Piezo-Patches

机译:基于教学学习的优化(TLBO),用于压电贴片的最佳放置

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Active vibration control using piezopatches has been active field for the past few years. Various methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc. had been used for optimal placement of piezopatches to control vibration of a cantilever beam. In the present study an attempt is made to find optimal location for placement of both single and multiple (i.e. 5 patches) piezo-patches on the cantilever beam. An advanced optimization technique known as Teaching Learning Based Optimization (TLBO) algorithm is used. The objective function used in this study is based on the strain equation of the cantilever beam. It is found that both the advanced optimization techniques i.e. TLBO and GA has given the maximum strain value at the root of the cantilever beam for the first six modes in case of single patch. Also for multiple patches the optimal locations obtained by TLBO is almost the same as that obtained by GA.
机译:在过去的几年中,使用压电补丁的主动振动控制一直是活跃的领域。遗传算法(GA),粒子群优化(PSO)等各种方法已用于优化压电贴片的位置,以控制悬臂梁的振动。在本研究中,尝试寻找最佳位置以将单个和多个(即5个贴片)压电贴片放置在悬臂梁上。使用了一种先进的优化技术,称为基于教学学习的优化(TLBO)算法。在这项研究中使用的目标函数基于悬臂梁的应变方程。发现在单个贴片的情况下,对于前六个模式,两种先进的优化技术,即TLBO和GA都给出了悬臂梁根部的最大应变值。同样,对于多个补丁,通过TLBO获得的最佳位置与通过GA获得的最佳位置几乎相同。

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