Supplementary Material: Dynamical Systems

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來自 University of Pennsylvania 的課程

Robotics: Aerial Robotics

1885 個評分

University of Pennsylvania

Robotics: Aerial Robotics

1885 個評分

課程 1（共 6 門，Specialization Robotics）

How can we create agile micro aerial vehicles that are able to operate autonomously in cluttered indoor and outdoor environments? You will gain an introduction to the mechanics of flight and the design of quadrotor flying robots and will be able to develop dynamic models, derive controllers, and synthesize planners for operating in three dimensional environments. You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments. Finally, you will gain insights through seeing real world examples of the possible applications and challenges for the rapidly-growing drone industry. Mathematical prerequisites: Students taking this course are expected to have some familiarity with linear algebra, single variable calculus, and differential equations. Programming prerequisites: Some experience programming with MATLAB or Octave is recommended (we will use MATLAB in this course.) MATLAB will require the use of a 64-bit computer.

從本節課中

Introduction to Aerial Robotics

Welcome to Week 1! In this week, you will be introduced to the exciting field of Unmanned Aerial Robotics (UAVs) and quadrotors in particular. You will learn about their basic mechanics and control strategies and realize how careful component selection and design affect the vehicles' performance. This week also provides you with instructions on how to download and install Matlab. This software will be used throughout this course in exercises and assignments, so it is strongly recommended to familiarize yourself with Matlab soon. Tutorials to help you get started are also provided in this week.

Basic Mechanics4:27

Dynamics and 1-D Linear Control10:29

Design Considerations3:37

Design Considerations (continued)4:33

Agility and Maneuverability7:00

Component Selection4:24

Effects of Size4:38

Supplementary Material: Introduction3:19

Supplementary Material: Dynamical Systems3:15

Supplementary Material: Rates of Convergence3:20

與講師見面

Vijay Kumar
Nemirovsky Family Dean of Penn Engineering and Professor of Mechanical Engineering and Applied Mechanics
School of Engineering and Applied Science

In this segment, we'll formally define a dynamical system and

look at the mathematical models of some example dynamical systems.

A dynamical system is a system in which the effects of input actions

do not immediately affect the system.

For example, if you turn on the thermostat in a cold room,

the temperature in the room will not immediately rise to the set temperature.

It will take some time for the room to actually heat up.

Similarly, if you push the gas pedal in your car, it takes time for

your car to speed up to the desired velocity.

Every dynamical system is defined by its state, which is a collection of variables

that completely characterizes the motion of a system.

The most common states, are the positions and

velocities of physical components of the system.

The states of a system are commonly denoted by the variable x.

As we've seen in lecture,

we use the notation x of t to describe the values of a system's states over time.

This function x of t is often characterized

by a set of governing ordinary differential equations.

The order of a system refers to the highest derivative that appears

in governing equations.

In lecture, we analyzed the dynamical system, x double dot equals u.

We see that the second derivative of x is the highest derivative that appears in

this equation.

Therefore, x double dot equals u represents a second order system.

Next, we'll see a few more examples of dynamical systems and

how they are modeled.

An example of a one-dimensional dynamical system is a mass on a string.

The mass can only move backwards and forwards in the y direction.

The mass's position is governed by the following ordinary differential equation.

We see that the highest derivative of y to appear

in the equation is the second derivative, making this a second order system.

The state of the system is the position and velocity of the mass along the y axis.

The input to this system is an additional force on the mass.

Inverted pendulum on a cart is an example of a two dimensional dynamical system.

Here, the cart is allowed to drive along the y direction

while the pendulum is simultaneously allowed to fall.

The motion of this system to be modeled by the following set

of coupled ordinary differential equations.

We see that once again the second derivatives of the cart position and

pendulum angle are the highest derivatives to appear in the equations,

making this a second order system.

There are four states in this system.

The first two are the positions of the cart and the pendulum and

the last two are their velocities.

The input is an additional force on the cart itself.

Here, we assume that we cannot directly apply a force to the pendulum.

Finally, considered the Quadrotor.

In this weeks lecture,

we only look at the motion of the Quadrotor in the Z direction.

As a result, we were able to model it as a one dimensional system.

However, it turns out that to completely characterize a motion of the Quadrotor,

we need to know its xyz position and

angular orientation, as well as its linear and angular velocities.

We'll talk more about the dynamic equations of the quadrotor

in the coming weeks.

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