MATLAB Simulation Modeling: Application and Model Design of Simulink and Stateflow

 

Chapter 1: Introduction

In today's scientific research and engineering fields, simulation modeling is a very important technology. Through simulation modeling, we can create a model on the computer that simulates the behavior of the real system, and optimize the system design through the analysis and testing of the model. MATLAB is a powerful tool widely used in science and engineering, and Simulink and Stateflow are two important tools in MATLAB for system-level simulation and state machine modeling. This article will introduce the basic concepts and applications of Simulink and Stateflow, and demonstrate their role in simulation modeling and model design methods through cases.

Chapter 2: Basic knowledge of Simulink

Simulink is a simulation environment based on graphical modeling, which uses the block diagram (Block Diagram) to represent the system model. In Simulink, a system model can be built by dragging and connecting various predefined blocks. These blocks represent different system components such as sensors, actuators, controllers, etc. Simulink also provides a wealth of signal processing, system control and simulation analysis tools, making model establishment and simulation easy and intuitive.

In order to better understand the application of Simulink, we take a simple DC motor control system as an example. We can use Simulink to build the simulation model of the system, and optimize the control algorithm of the motor through the simulation analysis of the model. Here is an example model:

% Example Model: DC Motor Control System

clear;

clc;

% create an empty model

model = 'dc_motor_control';

open_system(new_system(model));

% add block

add_block('simulink/Sources/Sine Wave', [model '/Reference']);

add_block('simulink/Continuous/Transfer Fcn', [model '/Plant']);

add_block('simulink/Commonly Used Blocks/Scope', [model '/Scope']);

% Connector

add_line(model, 'Reference/1', 'Plant/1');

add_line(model, 'Plant/1', 'Scope/1');

% Setting parameters

set_param([model '/Reference'], 'Frequency', '0.1');

set_param([model '/Plant'], 'Numerator', '10', 'Denominator', '[1 2 10]');

% save and run the model

save_system(model);

sim(model);

By running the above code, we can create a DC motor control system in Simulink with sine wave input, transfer function model and oscilloscope. By running the model in simulation, we can observe the dynamic behavior of the motor output response and improve the control performance by tuning the model parameters.

 

Chapter 3: Stateflow State Machine Modeling

Stateflow is a state machine modeling tool in MATLAB that allows users to graphically describe the state transitions and behavior of a system. Through Stateflow, we can model and analyze complex systems, especially for event-driven systems.

Let us take a simple traffic light control system as an example to introduce the application of Stateflow. In this system, the signal light can change state between red light, yellow light and green light, and change state according to traffic rules. The following is an example of a basic Stateflow state machine model:

% Example Model: Traffic Light Control System

clear;

clc;

% create an empty model

model = 'traffic_light_control';

sf = Stateflow(new_system(model));

% add state and transition

red = sf.addState('red');

yellow = sf.addState('yellow');

green = sf.addState('green');

red2yellow = sf.addTransition(red, yellow);

yellow2green = sf.addTransition(yellow, green);

green2red = sf.addTransition(green, red);

% Set conversion conditions and actions

red2yellow.setCondition('timer >= 30');

red2yellow.setAction('timer = 0');

yellow2green.setCondition('timer >= 10');

yellow2green.setAction('timer = 0');

green2red.setCondition('timer >= 20');

green2red.setAction('timer = 0');

% add timer variable

timer = sf.addData('timer', 0);

% set model parameters

sf.setInitialState(red);

% save and run the model

sf.save(fullfile(pwd, [model '.mdl']));

sim(model);

With the above code, we have created a traffic light control system in Stateflow that includes red, yellow, and green light states and corresponding state transitions. In the model, we used a timer variable to simulate the passage of time, and controlled transitions between states by setting transition conditions and actions. Through the simulation operation of the model, we can observe the change of the signal light state and the corresponding action execution.

Chapter 4: Joint Application of Simulink and Stateflow

 

Simulink and Stateflow can be used jointly to achieve more complex system modeling and simulation. Simulink provides rich system-level modeling and simulation functions, while Stateflow focuses on event-driven state machine modeling. By combining the two, we can more fully describe and analyze system behavior.

Let us consider a control system for a self-driving car. In this system, Simulink can be used to model the vehicle's dynamic behavior and environmental perception, while Stateflow can be used to model advanced driving decisions and control strategies. Here is a simplified example model:

% Example Model: Autonomous Vehicle Control System

clear;

clc;

% create an empty model

model = 'autonomous_vehicle_control';

open_system(new_system(model));

% Add Simulink module

add_block('simulink/Sources/Constant', [model '/Speed Reference']);

add_block('simulink/Sources/Constant', [model '/Distance Reference']);

add_block('simulink/Continuous/Integrator', [model '/Speed Integrator']);

add_block('simulink/Continuous/Transfer Fcn', [model '/Vehicle Dynamics']);

add_block('simulink/Continuous/Transfer Fcn', [model '/Environment Dynamics']);

add_block('simulink/Discrete/State-Space', [model '/Control Strategy']);

% Add Stateflow state machine

sf = Stateflow([model '/Decision Logic']);

red = sf.addState('red');

green = sf.addState('green');

stop = sf.addState('stop');

% Set conversion conditions and actions

red2green = sf.addTransition(red, green);

green2stop = sf.addTransition(green, stop);

stop2red = sf.addTransition(stop, red);

red2green.setCondition('distance > distance_threshold && speed > speed_threshold');

red2green.setAction('distance_threshold = distance + 100; speed_threshold = speed + 10');

green2stop.setCondition('distance < distance_threshold || speed < speed_threshold');

green2stop.setAction('distance_threshold = distance - 100; speed_threshold = speed - 10');

stop2red.setCondition('timer >= 60');

stop2red.setAction('timer = 0');

% set model parameters

set_param([model '/Speed Reference'], 'Value', '20');

set_param([model '/Distance Reference'], 'Value', '500');

set_param([model '/Vehicle Dynamics'], 'Numerator', '10', 'Denominator', '[1 2 10]');

set_param([model '/Environment Dynamics'], 'Numerator', '5', 'Denominator', '[1 5]');

set_param([model '/Control Strategy'], 'A', '-0.1', 'B', '1', 'C', '1', 'D', '0');

sf.setInitialState(red);

sf.addData('distance_threshold', 0);

sf.addData('speed_threshold', 0);

sf.addData('timer', 0);

% link module

add_line(model, 'Speed Reference/1', 'Speed Integrator/1');

add_line(model, 'Speed Integrator/1', 'Vehicle Dynamics/1');

add_line(model, 'Distance Reference/1', 'Control Strategy/1');

add_line(model, 'Vehicle Dynamics/1', 'Control Strategy/2');

add_line(model, 'Control Strategy/1', 'Environment Dynamics/1');

add_line(model, 'Environment Dynamics/1', 'Decision Logic/1');

% save and run the model

save_system(model);

sim(model);

The above code shows the modeling process of a self-driving car control system. The Simulink part includes the vehicle dynamics model, the environment dynamics model and the control strategy module, while the Stateflow part is used to describe the driving decision logic. Through the simulation operation of the model, we can observe that the self-driving car makes corresponding decisions and behaviors according to changes in the environment and control strategies.

Chapter 5: Summary and Outlook

Through the introduction of this article, we understand the basic concepts and applications of Simulink and Stateflow in MATLAB.

Simulink is a tool for system-level modeling and simulation, which provides a graphical interface and a rich module library, making system modeling simple and intuitive. We demonstrate the basic usage of Simulink through an example of a DC motor control system, including steps such as adding blocks, connecting blocks, setting parameters, and running the model.

Stateflow focuses on event-driven state machine modeling, which allows us to describe the state transition and behavior of the system in a graphical way. Through an example of a traffic light control system, we show the application of Stateflow, including operations such as adding states, transitions, and setting conditions and actions. The advantage of Stateflow is that it can describe complex state transition logic more clearly, and is especially suitable for modeling events-driven systems.

At the same time, Simulink and Stateflow can be used jointly to achieve more complex system modeling and simulation. Using an autonomous vehicle control system as an example, we show how Simulink and Stateflow can be combined to model vehicle dynamics and control strategies, respectively. Through joint application, we can more comprehensively describe and analyze system behavior, and carry out system performance optimization and decision logic design.

Simulink and Stateflow are powerful simulation modeling tools in MATLAB, and they have a wide range of applications in scientific research and engineering fields. Through Simulink and Stateflow, we can build system models in a graphical way, and improve system design through simulation analysis and optimization. In the future, with the continuous development of science and technology, Simulink and Stateflow will continue to play an important role in various fields, helping people solve more complex problems and challenges.