Research on Model-driven Adaptive Control Systems based on the Error of Closed-loop Parameters

基于闭环参数误差的模型驱动自适应控制系统研究

基本信息

批准号：
14350225
负责人：
KIMURA Hidenori
金额：
$ 4.61万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2003
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-14350225/
关键词：
Model-Driven control Adaptive control Complicated system 2-degrees of freedom control system control biology quantum control system モデル駆動型制御むだ時間

项目摘要

The purpose of the research is to formulate a new method of adaptive control called "Model-driven control" from a control theoretical point of view and to establish a theory for. analysis and synthesis as powerful control design framework. We have revealed significances and its properties of model-driven control architecture from theoretical and experimental aspects. A fundamental concept of model-driven control comes from a motor control of human. In this research, in order to provide new developments of method for dealing with "Complicated System" for both fields of adaptive control and motor control we tried the following three themes mainly (1) The formulation of model-driven adaptive control and the relation to motor control, (2) The establishment of identification method for complicated systems and the relation to learning control and (3) Control theoretical approaches to various nonlinear complicated systemsIn (1), we formulated a well-known "Feedback Error Learning method" as two-degrees of freedom control systems and investigated its closed-loop structure. A stability of the closed-loop system including adaptation and a convergence of its adaptation parameters were shown theoretically. Furthermore, these results were extended to a case where a time delay exists in the feedback loop. Each, result in (2) includes interest approaches to the essential difficulties for dealing with complicated systems. In (3), we obtained various outcomes which related to various complicated system, e.g., nonlinear robot arm, a hear exchanger in a fuel cell, control biology, biomechanics of human motor control, quantum control systems, a large scale network system and so on. In particular, results on control biology and quantum control system moved forward the frontier of new field in control theory.

该研究的目的是从控制理论的角度制定一种称为“模型驱动控制”的自适应控制方法，并为建立理论。分析和合成作为强大的控制设计框架。我们从理论和实验方面揭示了模型驱动控制体系结构的意义及其特性。模型驱动控制的基本概念来自人类的运动控制。在这项研究中，为了提供针对自适应控制和运动控制领域“复杂系统”的方法的新发展，我们主要尝试了以下三个主题（1）模型驱动的自适应控制以及与电动机控制，（2）建立复杂系统的识别方法以及与学习控制的关系以及（3）控制各种非线性复杂系统（1）的理论方法，我们提出了一种众所周知的“反馈错误学习方法”为两个 - 自由控制系统的学位，并研究了其闭环结构。理论上显示了闭环系统的稳定性，包括适应性和其适应参数的收敛性。此外，这些结果扩展到反馈循环中存在时间延迟的情况。每个结果（2）都包括解决复杂系统的基本困难的兴趣方法。在（3）中，我们获得了各种与各种复杂系统相关的结果在。特别是，控制生物学和量子控制系统的结果向前迈进了新领域的控制理论。