Collaborative Research: Multivariable Fixed Order Controller Synthesis and Design from Data

协作研究：基于数据的多变量固定阶控制器综合与设计

• 批准号: 0927652
• 负责人: S. Bhattacharyya
• 金额: $ 22.58万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0927652&HistoricalAwards=false
• 关键词: Collaborative; Research; Multivariable; Fixed; Order

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This research aims to develop new fundamental theory and effective design methodologies to address practically important issues that existing control theories cannot effectively handle: specifically the proposal will develop methods for the design of fixed and low order controllers for multivariable systems which satisfy multiple design specifications and which are based on the measured data and not on models. This is a very realistic engineering problem that remains open despite significant progress in computer-aided design. The objective of feedback control system design is to precisely regulate physical quantities such as position, velocity, temperature, pressure, flow-rate and level in dynamic systems despite significant uncertainty and knowledge of the behavior of the underlying processes. The present proposal approaches the control design problem with a view to overcoming several outstanding challenges. These are a) the need to develop design methods which use measured data directly since mathematical models are unavailable for most systems b) the design of low order or low complexity controllers since the existing methods yield high order controllers which are often fragile or acutely sensitive and therefore unimplementable and c) designs which satisfy multiple user defined specifications. Preliminary results on integral and first order controllers look promising. The research should have a significant impact on control applications ranging from chemical processes, manufacturing systems, disk drives, missile and aircraft control, internet congestion control and biological control systems including genetic networks. In each of these applications models are scarcely available and few effective design techniques exist for the effective design of simple controllers.

该奖项是根据2009年的《美国恢复和再投资法》（公法111-5）资助的。本研究旨在开发新的基本理论和有效的设计方法，以解决现有控制理论无法有效处理的实际重要问题：具体的建议将开发针对固定和低订单控制器的设计方法，这些方法是针对模型和基于数据的多元订单控制器的设计，并基于数据的确定性和衡量数据的确定性，并基于数据的确定性和衡量数据。这是一个非常现实的工程问题，尽管计算机辅助设计取得了重大进展，但仍保持开放。反馈控制系统设计的目的是精确调节身体数量，例如位置，速度，温度，压力，流量，流量率和动态系统中的水平，尽管有很大的不确定性和对基础过程的行为的了解。本提案解决了控制设计问题，以克服一些杰出的挑战。 这些是a）需要开发直接使用测量数据的设计方法，因为数学模型对于大多数系统不可用b）低阶或低复杂性控制器的设计，因为现有方法产生的高阶控制器通常是脆弱或非常敏感的，因此无法实现，因此无法实现，并且C）设计满足多重用户定义的设计。积分和一阶控制器的初步结果看起来很有希望。 这项研究应对从化学过程，制造系统，磁盘驱动器，导弹和飞机控制，互联网拥塞控制和包括遗传网络在内的生物控制系统等涉及的控制应用产生重大影响。在这些应用中的每一个中，几乎都无法使用，并且对于简单控制器的有效设计而言，几乎没有有效的设计技术。