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Multi-objective optimization of an active constrained layer damping treatment for shape control of flexible beams

机译:柔性梁形状控制的主动约束层阻尼处理的多目标优化

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This work presents the use of a multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem for the shape control of flexible beams with an active constrained layer damping (ACLD) treatment. The design objectives are to minimize the total weight of the system, the input voltages and the steady-state error between the achieved and desired shapes. Design variables include the thickness of the constraining and viscoelastic layers, the arrangement of the ACLD patches, as well as the control gains. In order to set up an evaluator for the MOGA, the finite element method (FEM), in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model a clamped-free beam with ACLD patches to predict the dynamic behaviour of the system. As a result of the optimization, reasonable Pareto solutions are successfully obtained. It is shown that ACLD treatment is suitable for shape control of flexible structures and that the MOGA is applicable to the present integrated optimization problem.
机译:这项工作提出了使用多目标遗传算法(MOGA)来解决采用主动约束层阻尼(ACLD)处理的柔性梁形状控制的集成优化问题。设计目标是使系统的总重量,输入电压以及所达到的形状和所需形状之间的稳态误差最小。设计变量包括约束层和粘弹性层的厚度,ACLD贴片的布置以及控制增益。为了建立MOGA的评估器,有限元方法(FEM)与Golla-Hughes-McTavish(GHM)方法结合使用,对带有ACLD斑块的无夹持梁建模以预测动态行为系统的。优化的结果是成功获得了合理的Pareto解。结果表明,ACLD处理适用于柔性结构的形状控制,而MOGA适用于当前的集成优化问题。

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