Robust Structural Control for Hazard Mitigation Using Probable and Possible Models

使用可能和可能模型进行减灾的稳健结构控制

• 批准号: 9503370
• 负责人: Gary Balas
• 金额: $ 12.5万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-07-01 至 1998-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9503370&HistoricalAwards=false
• 关键词: Robust; Structural; Control; Hazard; Mitigation

9503370 Balas The project will examine two approaches to control design for structures subjected to environmental as well as small- to-moderate dynamic loads. The first approach is to use "hard-bounds" or norms to describe the possible models for a structure and its corresponding set of possible prediction errors with a goal of maximizing the robust performance of the controller for a given set of possible excitations. In the second approach probability distributions are placed over the sets of possible models as well as over the possible excitations. This probable-model approach to control design introduces an extra dimension to the first approach that will yield additional information about the possible performance of the controller, such as the probability of achieving various levels of performance. Experimental data will be used to update and refine the control design models to improve the close-loop performance. The use of shape memory alloy actuators also will be investigated. Improvements in active and passive structural control can save lives and reduce structural damage and economic losses. ***

9503370 Balas该项目将研究两种方法来控制受环境以及小到中等动态负载的结构设计。 第一种方法是使用“硬边界”或范数来描述结构的可能模型及其相应的一组可能的预测误差，其目标是最大化控制器对于给定的一组可能激励的鲁棒性能。 在第二种方法中，概率分布被放置在可能的模型集以及可能的激励上。 这种控制设计的概率模型方法在第一种方法中引入了额外的维度，这将产生关于控制器可能性能的额外信息，例如实现各种性能水平的概率。 实验数据将被用来更新和完善控制设计模型，以提高闭环性能。 形状记忆合金驱动器的使用也将进行调查。 主动和被动结构控制的改进可以挽救生命，减少结构损坏和经济损失。 ***