Reduced Order Modeling
Reduced order modeling is a technique for reducing the computational complexity or storage requirements of a model while preserving the expected fidelity within a satisfactory error. Working with a surrogate reduced order model can simplify analysis and control design.
Reduced Order Modeling Basics
- Reduced Order Modeling
Reduce computational complexity of models by creating accurate surrogates.
- Nonlinear ARX Model of SI Engine Torque Dynamics
This example describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a nonlinear ARX model.
- Hammerstein-Wiener Model of SI Engine Torque Dynamics
This example describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a Hammerstein-Wiener model.
- Neural State-Space Model of SI Engine Torque Dynamics
This example describes reduced order modeling (ROM) of the nonlinear torque dynamics of a spark-ignition (SI) engine using a neural state-space model.
- 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. (Since 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
Identify a linear parameter varying reduced order model of a cascade of nonlinear mass-spring-damper systems.