Modelado de orden reducido
El modelado de orden reducido es una técnica para reducir la complejidad computacional o los requisitos de almacenamiento de un modelo al tiempo que se mantiene la fidelidad esperada dentro de un margen de error aceptable. Trabajar con un modelo de orden reducido puede simplificar el análisis y el diseño de control.
Puede crear modelos de orden reducido (ROM) de subsistemas modelados en Simulink, incluyendo modelos de simulación de orden completo y alta fidelidad de terceros. Puede utilizar estos modelos para simulación en escritorio en nivel de sistema, pruebas de hardware-in-the-loop (HIL), diseño de control y modelado de sensores virtuales.
Para crear un ROM de un modelo de Simulink o un subsistema del modelo utilizando un flujo de trabajo de IU, instale Reduced Order Modeling Support Package. Para obtener más información, consulte Reduced Order Modeling Support Package en File Exchange.
Temas
Conceptos básicos de modelado de orden reducido
- Reduced Order Modeling (System Identification Toolbox)
Reduce computational complexity of models by creating accurate surrogates.
Métodos basados en datos
- 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.
Métodos basados en linealización
- 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. (Desde R2023b) - 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.
Métodos basados en física
- 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. (Desde R2024b)
Información relacionada
- Modelado de orden reducido (System Identification Toolbox)
- Página de exploración Modelado de orden reducido
