Control Systems

R2026b

Design, test, and implement control systems

As a control systems engineer, you can use MATLAB^® and Simulink^® at all stages of development, including plant modeling, controller design, deployment with automatic code generation, and system verification. Using MATLAB and Simulink control systems products, you can:

Model linear and nonlinear plant dynamics using basic models, system identification, or automatic parameter estimation.
Trim, linearize, and compute frequency responses for nonlinear Simulink models.
Design controllers based on plant models using root locus, Bode diagrams, LQR, LQG, and other design techniques.
Interactively analyze control system performance using overshoot, rise time, phase margin, gain margin, and other performance and stability characteristics in time and frequency domains.
Automatically tune PID, gain-scheduled, and arbitrary SISO and MIMO control systems.
Design and implement robust and model predictive controllers or use model-free control methods such as model-reference adaptive control, extremum-seeking control, reinforcement learning, and fuzzy logic.
Deploy control algorithms to embedded systems for real-time control, tuning, or parameter estimation.
Design and test condition monitoring and predictive maintenance algorithms.

Products for Control Systems

Control System Toolbox

Design and analyze control systems

System Identification Toolbox

Create linear and nonlinear dynamic system models from input-output data

Predictive Maintenance Toolbox

Design and test condition monitoring and predictive maintenance algorithms

Robust Control Toolbox

Design robust controllers for uncertain plants

Model Predictive Control Toolbox

Design and simulate model predictive controllers

Fuzzy Logic Toolbox

Design and simulate fuzzy logic systems

Simulink Control Design

Linearize models and design control systems

Simulink Design Optimization

Analyze model sensitivity and tune model parameters

Reinforcement Learning Toolbox

Design and train policies using reinforcement learning

Motor Control Blockset

Design and implement motor control algorithms

C2000 Microcontroller Blockset

Design, simulate, and implement applications for Texas Instruments C2000 microcontrollers

STM32 Microcontroller Blockset

Design, simulate, and implement applications for STMicroelectronics STM32 microcontrollers

Raspberry Pi Blockset

Design, simulate, and deploy applications for Raspberry Pi.

Topics

Plant Modeling, System Identification, and Parameter Estimation

Control System Modeling with Model Objects (Control System Toolbox)
Build models that represent your control system using model objects.
Analyze Data in Multivariable Systems and Identify Models (System Identification Toolbox)
Collect MIMO data, estimate and compare models, and view corresponding model responses.
Online ARX Parameter Estimation for Tracking Time-Varying System Dynamics (System Identification Toolbox)
Perform online parameter estimation for a time-varying ARX model at the MATLAB command line.
Inverted Pendulum Parameter Estimation (Simulink Design Optimization)
Estimate multiple parameters of a model by iterated estimations.

Trimming, Linearization, and Frequency Response Estimation

Compute Operating Points from Specifications Using Steady State Manager (Simulink Control Design)
Find steady-state operating points that meet specifications by trimming your Simulink model using the Steady State Manager app.
Linearize Simulink Model at Model Operating Point (Simulink Control Design)
Linearize a model at its operating point consisting of initial state values and input signals.
Frequency Response Estimation in Model Linearizer Using Pseudorandom Binary Sequence (Simulink Control Design)
Identify a frequency-domain model for a high-frequency power electronics system using a PRBS input signal in Model Linearizer.

Control Design and Tuning

Root Locus Design (Control System Toolbox)
Design a compensator for an electrohydraulic servomechanism using root locus graphical tuning techniques.
Design Compensator Using Automated Tuning Methods (Control System Toolbox)
Tune a compensator using automated tuning methods in Control System Designer.
Tune Control Systems Using systune (Control System Toolbox)
Use systune to tune structured controllers for a simple application.
Design Controller for Power Electronics Model Using Simulated I/O Data (Simulink Control Design)
Use PID Tuner to identify a plant model and design a PID controller for a power electronics model that cannot be linearized.
Tune Gain-Scheduled Controller Using Closed-Loop PID Autotuner Block (Simulink Control Design)
Use Closed-Loop PID Autotuner block to tune gain-scheduled PID controller for a water-tank model in one simulation.

Predictive and Robust Control

Design MPC Controller in Simulink (Model Predictive Control Toolbox)
Design and simulate a model predictive controller for a Simulink model using MPC Designer.
Robust Control of Active Suspension (Robust Control Toolbox)
In this example, use H_∞ synthesis to design a controller for a nominal plant model. Then, use μ synthesis to design a robust controller that accounts for uncertainty in the model.

Adaptive and Intelligent Control

Model Reference Adaptive Control of Aircraft Undergoing Wing Rock (Simulink Control Design)
Design an MRAC controller that adapts disturbance model parameters to achieve performance matching an ideal reference model.
Design and Train Agent Using Reinforcement Learning Designer (Reinforcement Learning Toolbox)
Design and train a DQN agent for a cart-pole system using the Reinforcement Learning Designer app.
Design Controller for Artificial Pancreas Using Fuzzy Logic (Fuzzy Logic Toolbox)
Design and tune a FIS tree to control insulin infusion for type-1 diabetes.

Deployable Algorithms

Tune PI Controllers Using Field Oriented Control Autotuner Block on Real-Time Systems (Motor Control Blockset)
Compute the gain values of PI controllers within the speed and current controllers by using the Field Oriented Control Autotuner block.
Simulation and Code Generation Using Simulink Coder (Model Predictive Control Toolbox)
Simulate your MPC controller in Simulink and generate real-time code that uses either double-precision or single-precision signals.
Parameter Tuning for Digital Twins (Simulink Design Optimization)
Monitor the condition of an electric vehicle battery in the field with a deployed version of parameter estimation using Simulink Compiler™.

Featured Examples

Multiloop Control of a Helicopter

Use slTuner and systune to tune a multi-loop controller for a rotorcraft.

(Control System Toolbox)

Design PID Controllers for Three-Phase Rectifier Using Closed-Loop PID Autotuner Block

Tune controllers for a Vienna-rectifier-based power factor corrector using the Closed-Loop PID Autotuner block.

(Simulink Control Design)

Adaptive Cruise Control Using Extremum Seeking Control

Design an extremum seeking controller to adjust controller gains for an adaptive cruise control system.

(Simulink Control Design)

Control PMSM Loaded with Dual Motor (Dyno) Using C2000 Processors

Uses field-oriented control (FOC) to control two three-phase permanent magnet synchronous motors (PMSM) coupled in a dyno setup. Motor 1 runs in the closed-loop speed control mode. Motor 2 runs in the torque control mode and loads Motor 1 because they are mechanically coupled. You can use this example to test a motor in different load conditions.

(C2000 Microcontroller Blockset)

Lane Following Using Nonlinear Model Predictive Control

Design a lane-following controller using nonlinear MPC with road curvature previewing.

(Model Predictive Control Toolbox)

Rolling Element Bearing Fault Diagnosis

Perform fault diagnosis of a rolling element bearing based on acceleration signals.

(Predictive Maintenance Toolbox)

Videos

Understanding Kalman Filters
This video walks through common uses of Kalman filters. A Kalman filter is an optimal estimation algorithm used to estimate states of a system from indirect and uncertain measurements.

From PID Control to Adaptive Control: Systematically Designing Controllers in Simulink
This video shows how to use systematic and automated ways to quickly design and implement different types of controllers, ranging from PID controllers to model reference adaptive control to reinforcement learning.

Addressing Challenges Involved in Design and Implementation of Motor Control Algorithms
This video shows how to design and test motor control algorithms with the help of system level simulation, measure design complexity, and perform systematic unit testing including HIL verifications.