【Advanced】Building a Brushless DC Motor Simulation Based on MATLAB/Simulink

# 2.1 Basic Components and Principles of Simulink

## 2.1.1 Modules, Signal Lines, and Subsystems

Simulink models consist of modules, signal lines, and subsystems. Modules represent components or functions within a system, such as gains, integrators, transfer functions, etc. Signal lines connect modules, indicating the flow of data or signals between them. Subsystems allow complex models to be organized into smaller, manageable parts.

## 2.1.2 Data Types and Variables

Simulink supports a variety of data types, including scalars, vectors, matrices, and structures. Variables are used to store and manipulate data. They can be local variables (visible only within a specific subsystem) or global variables (visible throughout the entire model).

# 2. Basic Concepts of MATLAB/Simulink Modeling

## 2.1 Basic Components and Principles of Simulink

### 2.1.1 Modules, Signal Lines, and Subsystems

Simulink is a block-based modeling environment where models are composed of modules, signal lines, and subsystems.

**Modules** represent functional components within the system, such as gains, integrators, or transfer functions. Each module has input and output ports for connecting to other modules.

**Signal lines** are used to connect the input and output ports of modules, allowing data to flow between them.

**Subsystems** enable complex models to be broken down into smaller, reusable modules. Subsystems can be nested within other subsystems, thereby creating hierarchical model structures.

### 2.1.2 Data Types and Variables

Simulink supports a range of data types, including scalars, vectors, and matrices. Variables are employed to store and manipulate data and can have different scopes, such as local scope (visible only within a specific subsystem) or global scope (visible throughout the entire model).

## 2.2 MATLAB/Simulink Modeling Tips

### 2.2.1 Model Parametrization and Reusability

**Model parametrization** allows users to specify values for model parameters, enabling easy adjustments to model behavior. This is particularly useful for exploring different scenarios and optimizing model performance.

**Reusability** is achieved by creating reusable modules and subsystems that can be easily inserted into various models. This aids in saving time and ensuring consistency in models.

### 2.2.2 Simulation Parameter Settings and Optimization

**Simulation parameters** control the behavior of simulations, such as simulation time, step size, and solver options. Optimizing these parameters is crucial for obtaining accurate and efficient simulation results.

**Simulation optimization** involves adjusting simulation parameters to enhance simulation speed or accuracy. This can be achieved by adjusting the step size, using different solvers, or parallelizing simulations.

**Code block:**

% Set simulation parameters
sim_params = simset('SrcWorkspace', 'current', ...
                     'StartTime', 0, ...
                     'StopTime', 10, ...
                     'FixedStep', 0.001);

% Optimize simulation
sim_params = simset(sim_params, 'Solver', 'ode45', ...
                     'RelTol', 1e-4, ...
                     'AbsTol', 1e-6);

**Logical Analysis:**

This code block sets simulation parameters, including the source workspace, start time, stop time, and fixed step size. It also optimizes the simulation by specifying the solver, relative tolerance, and absolute tolerance.

# 3.1 Motor Modeling

## 3.1.1 Electromagnetic Equations and State Equations

The electromagnetic equations for a brushless DC motor are as follows:

v = R*i + L*di/dt + Ke*ω
T = Ke*i

Where:

- v is the motor terminal voltage
- i is the motor current
- R is the motor resistance
- L is the motor inductance
- Ke is the motor electromotive constant
- ω is the motor angular velocity
- T is