Analysis of Model Set and Time-Varying Control based on Complexity

基于复杂度的模型集与时变控制分析

• 批准号: 11450164
• 负责人: KIMURA Hidenori
• 金额: $ 4.29万
• 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: 1999
• 资助国家: 日本
• 起止时间: 1999 至 2001
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-11450164/
• 关键词: Model; System identification; 4SID Method; Complexity; Learning theory; Control Systems; Adaptive control; LPV control; 赤池情報量基準; モデル集合同定; サンプル複雑度; モデル駆動制御; 逆モデル; 運動制御; ブレンディングコントロール; 内部モデル制御; モデル集合; 複雑度; 情報量基準; スイッチング制御; 部分空間同定; 熱交換器

1 Real-Time Identification of Time-Varying SystemsWe are successful in obtaining sequential version of the 4-SID method which enables us to track the parameter variation of the system. We have clarified some fundamental structure of sequential 4-SID method from the viewpoint of realization theory. This is the most remarkable result we have obtained in this research program.2 Analysis and Quantification of Model Complexity and Its Relation to LearningWe have applied learning theory to identification of model set and obtained the lower and upper bound of the samples required for guaranteering a prescribed model accuracy. We have defined complexity of model set through this sample number bounds. We have pointed out that the prevailing stochastic analysis of model set identification connected with control has some drawback.3 Control Algorithm for Linear Parameter Varying (LPV) SystemsWe have obtained some fundamental results on this issue. H-infinity control structure and topological aspects of model set have been considered from the LPV point of view. Also, we have considered the application of the so-called just-in-time (JIT) modeling. However, we are unable to establish so far the solid control architecture and algorithm for LPV systems based on the results obtained in (1) and (2).

1实时识别时间变化的系统swe成功地获得了4-Sid方法的顺序版本，该方法使我们能够跟踪系统的参数变化。从实现理论的角度来看，我们已经阐明了连续4-Sid方法的一些基本结构。这是我们在本研究计划中获得的最显着的结果。2模型复杂性及其与学习关系的分析和量化将学习理论应用于模型集的识别，并获得了确保规定模型准确性所需的样本的下层和上限。我们已经定义了通过此示例数量界限设置的模型的复杂性。我们已经指出，与控制连接的模型集识别的主要随机分析具有一定的缺点。3线性参数变化（LPV）Systemswe的控制算法在此问题上获得了一些基本结果。从LPV的角度考虑了模型集的H-内控制结构和拓扑方面。另外，我们考虑了所谓的即时（JIT）建模的应用。但是，我们无法根据（1）和（2）中获得的结果来建立LPV系统的实体控制体系结构和算法。

项目成果

Nobuyama and Tsumura: "Recent Advance of Deadbeat Control of Continuous-Time Systems A Mixed Time/Frequency-Domain Control"Systems, Control and Information. Vol. 43, No. 5. (1999)

Nobuyama 和 Tsumura：“连续时间系统无差拍控制的最新进展——混合时域/频域控制”系统、控制和信息。

Nagamune and Yamamoto: "An Identification Method for a State Space Model Set with Time-Varying Parametric Uncertainties by Using the Subspace Method"Trans. Of the society of Instrument and Control Engineers. Vol.35. 741-747 (1999)

Nagamune和Yamamoto：“使用子空间方法识别具有时变参数不确定性的状态空间模型集”Trans。

津村幸治: "制御系設計を考慮したモデルの選択・推定"計測と制御. Vol.39. 757-762 (2000)

Koji Tsumura：“考虑控制系统设计的模型选择和估计”测量与控制卷 39. 757-762 (2000)。

Q.Zheng, H.Kimura: "Just-In-Time modeling for function prediction and its applications"Asian Journal of Control. Vol.3. 35-44 (2001)

Q.Zheng、H.Kimura：“功能预测的即时建模及其应用”亚洲控制杂志。

木村英紀: "H^∞制御"コロナ社. 255 (2000)

木村英树：《H^∞控制》Coronasha 255 (2000)