차수 축소 모델링

정확한 대리(surrogate)를 생성하여 Simulink^® 모델의 계산 복잡성 줄이기

차수 축소 모델링은 모델의 충실도를 허용 가능한 오차 범위 내에서 유지하면서 계산 복잡도나 저장공간 요구 사항을 줄이는 기법입니다. 차수 축소 모델을 사용하면 제어 설계 및 분석을 단순화할 수 있습니다.

전차수(full-order) 모델, 고충실도 모델, 타사 시뮬레이션 모델 등을 비롯하여 Simulink에서 모델링된 서브시스템의 ROM(차수 축소 모델)을 만들 수 있습니다. 그리고 만든 ROM을 시스템 수준의 데스크탑 시뮬레이션, HIL(Hardware-in-the-Loop) 테스트, 제어 설계, 가상 센서 모델링에 사용할 수 있습니다.

Reduced Order Modeler 앱은 Simulink 모델과 모델 내 서브시스템을 기반으로 ROM을 만들기 위한 UI 워크플로를 제공합니다. 이 앱을 사용하려면 애드온을 받고 관리하기의 지침에 따라 Reduced Order Modeler for MATLAB^® 지원 패키지를 설치하십시오.

앱

Reduced Order Modeler

Create reduced order models based on Simulink models, subsystems within models, or simulation data (R2025b 이후)

도움말 항목

차수 축소 모델링 기본 사항

Reduced Order Modeling Overview (System Identification Toolbox)
Reduce computational complexity of models by creating accurate surrogates.

데이터 기반 방법

Reduced Order Modeling of Electric Vehicle Battery System Using Neural State-Space Model (System Identification Toolbox)
This example shows a reduced order modeling (ROM) workflow, where you use deep learning to obtain a low-order nonlinear state-space model that serves as a surrogate for a high-fidelity battery model.
Surrogate Modeling Using Gaussian Process-Based NLARX Model (System Identification Toolbox)
In this example, you replace a hydraulic cavitation cycle model in Simulink with a surrogate nonlinear ARX (NLARX) model to facilitate faster simulation.
Physical System Modeling Using LSTM Network in Simulink (Deep Learning Toolbox)
This example shows how to create a reduced order model (ROM) that acts as a virtual sensor in a Simulink® model using a long short-term memory (LSTM) neural network.

선형화 기반 방법

LPV Approximation of Boost Converter Model (Simulink Control Design)
Approximate a nonlinear Simscape™ Electrical™ model using a linear parameter varying model.
Reduce Model Order Using Model Reducer App (Control System Toolbox)
Interactively reduce model order while preserving important dynamics.
Sparse Modal Truncation of Linearized Structural Beam Model (Control System Toolbox)
Compute a low-order approximation of a sparse state-space model obtained from linearizing a structural beam model. (R2023b 이후)
Specify Linearization for Model Components Using System Identification (Simulink Control Design)
You can use System Identification Toolbox™ software to identify a linear system for a model component that does not linearize well, and use the identified system to specify its linearization.
Reduced Order Modeling of a Nonlinear Dynamical System as an Identified Linear Parameter Varying Model (System Identification Toolbox)
Identify a linear parameter varying reduced order model of a cascade of nonlinear mass-spring-damper systems.
Approximate Nonlinear Behavior Using Array of LTI Systems (Simulink Control Design)
You can use linear parameter varying models to approximate the dynamics of nonlinear systems.

물리 기반 방법

Model an Excavator Dipper Arm as a Flexible Body (Simscape Multibody)
Use the Reduced Order Flexible Solid block to model a deformable body of arbitrary geometry. Start with the CAD geometry of the body, produce a finite-element mesh, and generate reduced-order data to use with the block.
Improve Simulation Speed of Power Electronics Systems with Reduced Order Modeling (Simscape Electrical)
This example shows how to enhance the model simulation speed of an electro-thermal DC-DC step-down converter by converting a high-fidelity switch to a reduced order model (ROM) switch. (R2024b 이후)