模型降阶
使用低阶模型可以简化分析和控制设计。相对于高阶模型，较简单的模型也更容易理解和操作。当您线性化复杂的 Simulink® 或 Partial Differential Equation Toolbox™ 模型、互连模型元素或使用其他过程以生成对您的应用尤为有用的动态特性影响不太大的状态时，您可以获得高阶模型。使用 Control System Toolbox™ 软件，您可以获得普通 LTI 模型或大型稀疏 LTI 模型的低阶模型。
要获得低阶模型，您可以：
使用各种方法和标准（如平衡截断和本征正交分解 (POD)）计算 LTI 模型或稀疏 LTI 模型的低阶逼近。使用
reducespec作为这些工作流的入口函数。
此外，您还可以通过使用 minreal、sminreal 或 xelim 等函数执行零极点对相消或消除低贡献状态来简化模型。
您还可以使用模型降阶器和实时编辑器中的模型降阶任务以交互方式减少模型阶数。
有关降低模型阶数的方法的详细信息，请参阅Model Reduction Basics。
App
| 模型降阶器 | Reduce complexity of linear time-invariant (LTI) models |
实时编辑器任务
| 模型降阶 | Reduce complexity of linear time-invariant (LTI) models in the Live Editor |
函数
对象
主题
模型简化工作流
- Model Reduction Basics
Model-order reduction can simplify analysis and control design by providing simpler models that are easier to understand and manipulate. - Task-Based Model Order Reduction Workflow
Learn how to create custom reduction criteria to obtain reduced-order models.
LTI 模型降阶
- Approximate Model by Balanced Truncation at the Command Line
Compute a reduced-order approximation of a model at the command line. - Compare Truncated and DC Matched Low-Order Model Approximations
Compute a low-order approximation in two ways and compare the results. - Approximate Model with Unstable or Near-Unstable Pole
Compute a reduced-order approximation of a system when the system has unstable or near-unstable poles. - Frequency-Limited Balanced Truncation
Reduce a high-order model by removing states of relatively low energy within a particular frequency interval.
稀疏 LTI 模型降阶
- Sparse Modal Truncation of Linearized Structural Beam Model
Compute a low-order approximation of a sparse state-space model obtained from linearizing a structural beam model. (自 R2023b 起) - Sparse Balanced Truncation of Thermal Model
Balanced truncation of a sparse state-space model obtained from linearizing a thermal model. (自 R2023b 起)
交互式工作流
- Import and Export Data in Model Reducer
Import and export model data in Model Reducer. - Reduce Model Order Using Model Reducer App
Interactively reduce model order while preserving important dynamics. - Balanced Truncation Model Reduction
Compute lower order approximations of higher order models by removing states with lower energy contributions.
- Modal Truncation Model Reduction
Reduce model order by eliminating poles that fall outside a specific frequency range.
- Proper Orthogonal Decomposition Using Model Reducer
Reduce model order using proper orthogonal decomposition in Model Reducer. (自 R2025a 起)
- Pole-Zero Simplification
Reduce model order by canceling pole-zero pairs or eliminating states that have no effect on the overall model response. - Visualize Reduced-Order Models in Model Reducer App
Examine and compare time-domain and frequency-domain responses of the original and reduced models. - Model Reduction in the Live Editor
Interactively perform model reduction and generate code in a live script using the Reduce Model Order task.
