The code representing these equations is below. These simulation uses the nonlinear equations of motion derived in the System Modeling section. The quad_dynamics_nonlinear.m function uses the initial values to calculate the initial velocities and accelerations of the quadrotor model. The values of the physical parameters are taken from the Model Verification section. In the next section, quad_variables.m is run which defines the primary variables for the simulation including the quadrotor’s physical parameters, initial and desired conditions, simulation parameters, and controller gains. The plot_quad_model.m script then draws the initial quadrotor model in the three dimensional plotting environment. mat file uses the quadrotor’s physical dimensions such as arm length, arm thickness, and propeller radius to define the vertices of each arm and motor in three dimensions for plotting purposes.

This script begins by loading the Quad_plotting_model.mat file which is created by running define_quad_model.m. The function init_plot.m draws the three dimensional environment which the quadrotor’s simulated movement will be visualized inside of. The global variable Quad is created which will hold all of the quadrotor variables. This file initializes the simulation environment with the following commands. The simulator is run using the quadrotor_sim.m file. Below is a brief description of the main files and functions required to run and manipulate the simulated quadrotor. It also allows the user to quickly generate data and manipulate the plotting of the data for analysis purposes. The Matlab scripts developed allow a user to quickly and easily make small modifications such as physical parameters or control gains as well as large changes such as the equations of dynamics or controller types. Where appropriate, specific code or blocks are highlighted in greater detail. The following sections outline the code necessary to implement a quadrotor simulator in MATLAB. An accurate simulation environment enables operators to design and test control designs, filters, observers, and path planning algorithms before they are implemented on the physical system in the real world. All of these pieces are necessary to create an accurate simulation environment. An accurate simulation combines the equations of motion derived in the System Modeling section, the specific parameter values estimated in the Model Verification section, and the controller designed in the Controller Design section. This page describes the details of developing a robust and accurate simulation environment. System Modeling ■ Model Verification ■ Controller Design ■ Simulation EnvironmentĪutopilot Implementation ■ System Set-up ■ Experiments

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