Modellazione a ordine ridotto
La modellazione a ordine ridotto è una tecnica per ridurre la complessità computazionale o i requisiti di archiviazione di un modello, preservando al contempo la fedeltà prevista entro un errore soddisfacente. Lavorare con un modello a ordine ridotto può semplificare l'analisi e la progettazione di controllo.
È possibile creare modelli a ordine ridotto (ROM) di sottosistemi modellati in Simulink, compresi modelli di simulazione di terze parti ad alta fedeltà e a ordine completo. È possibile utilizzare questi modelli per la simulazione desktop a livello di sistema, i test Hardware-In-the-Loop, la progettazione di controllo e la modellazione di sensori virtuali.
Per creare un ROM di un modello o di un sottosistema del modello di Simulink utilizzando un workflow dell'interfaccia utente, installare il pacchetto di supporto Reduced Order Modeling. Per ulteriori informazioni, vedere Reduced Order Modeling Support Package (Pacchetto di supporto della modellazione a ordine ridotto) in File Exchange.
Argomenti
Nozioni di base sulla modellazione a ordine ridotto
- Reduced Order Modeling (System Identification Toolbox)
Reduce computational complexity of models by creating accurate surrogates.
Metodi guidati dai dati
- 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.
Metodi basati sulla linearizzazione
- 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. (Da 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.
Metodi basati sulla fisica
- 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. (Da R2024b)
Informazioni complementari
- Modellazione di ordine ridotto (System Identification Toolbox)
- Reduced Order Modeling Discovery Page
