quadx88 0.3.1

Configurable dynamical model of quadcopter

QUADx88

Configurable dynamical model of quadcopter

API documentation

The Quadcopter Class

To construct a quadcopter object we can call the constructor

import quadx88 as qx

copter = qx.Quadcopter(mass=1.15, 
                       ts=0.0083,
                       prop_mass=0.01)

The following parameters can be provided to the above constructor

Parameter	Description	Default
ts	Sampling time (s)	1/125
mass	Total mass of the copter (kg)	1
arm_length	Arm length (m)	0.225
moi_xx	x-x moment of inertia	0.01788
moi_yy	x-x moment of inertia	0.03014
moi_zz	z-z moment of inertia	0.04614
gravity_acc	gravitational acceleration (m/s2)	9.81
air_density	air density (kg/m3)	1.225
K_v	Kv constant of motors (rpm/V)	1000
motor_time_constant	motor time constant (s)	0.05
rotor_mass	rotor mass (kg)	0.04
rotor_radius	rotor radius (m)	0.019
motor_mass	motor mass (kg)	0.112
voltage_max	maximum voltage (V)	16.8
voltage_min	minimum voltage (V)	15.0
thrust_coeff	thrust coefficient of propellers	0.112
power_coeff	power coefficient of propellers	0.044
prop_mass	propeller mass (kg)	0.009
prop_diameter_in	propeller diameter (inches)	10

Getters

Method/Property	Description
state	Nine dimensional state of the system, $x=(q, \omega, n)$
quaternion	Current quaternion
hover_rpm	Hovering spin in RPM
euler_angles()	Current Euler angles in degrees

Setters

To initialise the state of the system the following methods are available

1. set_initial_quaternion(q), where q is the quaternion
2. set_initial_angular_velocity(w)
3. set_initial_motor_spin(spin)
4. set_initial_euler_angles(yaw, pitch, roll, angle_unit=ANGLE_UNITS.RADIANS), where yaw, pitch and roll and the three Euler angles (in this order) and angle_unit is the units of measurement. For example to construct a quadcopter object with an initial attitude of $\phi=0^\circ$, $\theta=1^\circ$ and $\psi=-5^\circ$ we can do

copter = qx.Quadcopter()
copter.set_initial_euler_angles(-5, 0, 1, ANGLE_UNITS.DEGREES)

System matrices

The methods continuous_linearised_matrices and discrete_linearised_matrices return a dictionary with the continuous-time and discrete-time matrices of the linearised system respectively. The discrete-time linearised system has the form $$\begin{aligned} x_{t+1} =& Ax_t + Bu_t, \ y_t =& Cx_t. \end{aligned}$$

Nonlinear dynamical system

To simulate the nonlinear model you can use move(u) which updates the system state following the application of a given control action for the duration of the sampling time.