Control Systems

Capabilities for Modeling Dynamic Systems

Explore what is possible with MATLAB and Simulink

Tweet

Download hi-res poster

Explore capabilities:

Model Structures

Model Parameters

Model Manipulation

Model Structures

Transfer Function - What Is a Transfer Function? - Discovery
State Space
What Is State Space? - Documentation
Uncertain State-Space Model - Function
Sparse First-Order State-Space Model - Function
Sparse Second-Order State-Space Model - Function
Linear Parameter Varying - LPV and LTV Models - Documentation
Time Series (ARX, ARMA) - Time Series Analysis - Documentation
ZPK - Zero-Pole-Gain Model - Function
Frequency Response Data - Frequency Response Data Models - Documentation

Nonlinear Models

ODEs
Getting Started with Simulink for Controls (11:30) - Video
Thermal Model of a House in Simulink - Example
ODEs with Symbolic Math - Documentation
Solving ODEs in MATLAB - Video Series
Neural State Space
Neural State-Space Models - Documentation
Reduced Order Modeling for Jet Engine Turbine Blade (8:14) - Video
Hammerstein-Weiner - Hammerstein-Wiener Models - Documentation
NLARX
Nonlinear ARX Models - Documentation
Estimate Nonlinear Models of Dynamic Systems (5:31) - Video

AI-Based Models

Gaussian Process - Gaussian Process Regression Mapping Function - Function
Support Vector Machine - Support Vector Machine Regression Mapping Function - Function
Regression Tree - Decision Tree Ensemble Mapping Function - Function
Neural Network - Multilayer Neural Network Mapping Function - Function

Integrate Third-Party Models

Functional Mockup Unit - FMU Importing - Documentation
S-Functions - What Is an S-Function? - Documentation
Python Importers
Integrate Python Code with Simulink - Documentation
Import Pretrained Networks from External Platforms - Documentation
Call Python from MATLAB - Documentation

Model Parameters

First Principles
ODEs in MATLAB - Documentation
ODEs in Simulink - Documentation
Physical Modeling - Physical Modeling - Solution

Parameter Estimation App - Parameter Estimator - App
Grey Box ODEs - Grey-Box Model Estimation - Documentation
Parameter Estimation in Simulink
Estimate Model Parameters and Initial States from Data - Documentation
How to Estimate Model Parameters from Test Data with Simulink (4:13) - Video

System Identification App - System Identification - App
Data Preparation - Data Preparation - Documentation
Offline Estimation
Linear Model Identification - Documentation
Nonlinear Model Identification - Documentation
Model Analysis - Model Analysis - Documentation
Online Estimation - Online Parameter Estimation - Documentation

Model Manipulation

Model Transformation - Model Transformation - Documentation
Model Type - Model Type Conversion - Documentation
Continuous-Discrete - Continuous-Discrete Conversion - Documentation
State-Coordinate - State-Coordinate Transformation - Documentation
Modal Decomposition - Modal and Spectral Decompositions

Linearization
Linearization Basics - Documentation
What Is Linearization? - Documentation
Numerical Perturbation - Linearization Methods - Documentation
Block-by-Block - Exact Linearization Algorithm - Documentation
Frequency Response Estimation - Estimate and Examine Frequency Response Characteristics - Documentation
Model Linearizer App - Model Linearizer - App

Reduced Order Modeling
What Is Reduced Order Modeling? - Discovery
Reduced Order Modeler - App
Balanced Truncation - Balanced Truncation Model Reduction - Documentation
Pole-Zero Simplification - Pole-Zero Simplification - Documentation
Model Reducer App
Model Reducer - App
Model Order Reduction with the Model Reducer App (4:57) - Video
Data-Driven ROM
Reduced Order Modeling - Video Series
Reduce Complexity of Models by Creating Accurate Surrogates - Documentation

Brian Douglas

Have questions?

Contact the Modeling Dynamic Systems expert.

Email Brian