Explore
For Enterprise
Join for Free
Log In

艺术与人文
商务
计算机科学
数据科学
信息技术
健康
数学和逻辑
个人发展
物理科学与工程
社会科学
语言学习
学位
证书

浏览 Coursera 的全部课程

浏览
搜索
企业版
登录
免费加入

4: Definitions: Lyapunov Stability

Loading...

Control of Nonlinear Spacecraft Attitude Motion

科罗拉多大学波德分校

4.7（27 个评分） | 3.4K 名学生已注册

课程 3（共 4 门，Spacecraft Dynamics and Control 专项课程）

This course trains you in the skills needed to program specific orientation and achieve precise aiming goals for spacecraft moving through three dimensional space. First, we cover stability definitions of nonlinear dynamical systems, covering the difference between local and global stability. We then analyze and apply Lyapunov's Direct Method to prove these stability properties, and develop a nonlinear 3-axis attitude pointing control law using Lyapunov theory. Finally, we look at alternate feedback control laws and closed loop dynamics. After this course, you will be able to... * Differentiate between a range of nonlinear stability concepts * Apply Lyapunov’s direct method to argue stability and convergence on a range of dynamical systems * Develop rate and attitude error measures for a 3-axis attitude control using Lyapunov theory * Analyze rigid body control convergence with unmodeled torque

查看授课大纲

审阅

4.7（27 个评分）

5 stars
22 ratings
4 stars
3 ratings
2 stars
2 ratings

从本节课中

Nonlinear Stability Definitions

Discusses stability definitions of nonlinear dynamical systems, and compares to the classical linear stability definitions. The difference between local and global stability is covered.

3: Definitions: Lagrange Stability3:33

4: Definitions: Lyapunov Stability5:50

5: Definitions: Asymptotic Stability6:17

6: Definitions: Global Stability2:27

7: Linearizing a Dynamical System6:10

Optional Review: Stability Definitions16:18

教学方

Hanspeter Schaub
Glenn L. Murphy Chair of Engineering, Professor

脚本

Lyapunov stability, this is the one if you just hear somebody talk about

the non-linear system and say, is that stable?

They're implying Lyapunov stability.

And that's a very common assumption.

There's many, many control papers, all vigorous math and

they just talk stability.

It's implied to be Lyapunov stability.

So what Lyapunov stability mean?

Here we think of this in state space.

If you have this ball that can move around the reference, it generates this tube.

This tube size was not a function of the initial conditions.

Now with the Lyapunov stability, what we're saying is,

we're getting in there, but you can pick an epsilon.

That means you have a size of a ball at the end,

if I want to be within one degree of the attitude.

There is a neighborhood you have tp be within ten degrees to begin with and

then I can guarantee at some point you enter one degree and

stay in one degree all right.

So you get to pick epsilon Lyapunov stability you don't get to pick a bound

it just comes form the physics of the problem independent of initial conditions.

Here you get to pick the bound, and say hey, if I want to be within one,

this is what you have to do.

A simple example is actually the spring mass system.

It's an oscillatory response.

If I want to guarantee my wiggles are less than one-degree and

I'm letting it go with zero rates just make sure you deflect that pendulum less

than one degree and you're guaranteed you're staying in there.

Their epsilon delta happens to be the same.

Don't always have to be the same.

But that's kind of the stability argument that we have here.

There's no guarantee you could make epsilon as small as you want.

I can make it a millionth of a degree, great.

And it's going to stay within a millionth of a degree.

Three's no guarantee it'll converge, all right?

So while we can make epsilon arbitrary small, we can't make epsilon zero.

because that would imply convergence that you have, right?

There's just a bound that I can guarantee that once I get within it,

and you could make that aperture very small, so

it gives you the kind of neighborhood, within what neighborhood would I converge

to within some tolerance of your pointing accuracy as an example.

Good, these are three definitions, any questions on the neighborhood?

Lagrange, think bounded doesn't depend on initial conditions.

The Lyapunov stability, the final tube that you have, that ball

that moves through around the reference, depends on initial conditions, right?

That's why we have to stay within some accuracy, but you can pick these now.

So, let's talk about an example and apply this.

We've seen Robert was talking earlier about spins, about intermediate access.

If you remember that video I showed from the astronaut taking that one little key,

spinning it, right.

It looked stable.

In simple layman's terms.

It was kind of wobbling, but it was there.

But then one happened?

Trevor. >> Flipped.

>> It flipped, okay, then it looked stable.

Did it stay there?

No, it kept flipping back again, right?

And from the pole hold plots, we understand why it's doing that.

And as we get closer to separate trigs that's pure spin.

It hangs out a long time there, and the pole plot time slows down, so to speak.

And then it goes quickly across.

So that's a situation where if you looked at a short period of time,

you might go well, wait a minute.

It wobbled maybe within five degrees.

That would be the stable.

But that is not true.

Because while it was here for a while, at some point and with these panes we know

we wait long enough it will eventually leave again, it leaves as to begin.

That's why the separate x motion when you're doing this looks kind of stable for

short period of time that's not stable.

It's still unstable because at some point it's going to leave this

neighbourhood again.

All these definitions, all these stability definitions mean once you enter

this desired stuff, you're proving that it will remain in there forever.

So the spring amper system with gravity.

Once you let go and it wiggles, it stays here.

It is there, it's never going to all of a sudden have a huge oscillation again.

That's not in this dynamical system.

But the seperate nix motion does do that.

So that's why you have to be careful if you use numerical tools, because if we try

to publish and go well I ran six million simulations and I ran them all for five

seconds and within five seconds everything kind of wiggled and stayed close.

Must be stable they're going to be yelling at you going wait you need to sim it way

longer and really prove this.

And if you can do it analytically you avoid all those arguments, right?

So these definitions are nice.

You need to be familiar with the basics, I like the visual part of it.

The wordings you going to remember great but

just, these are the concepts you have, proving this is a realm pain.

We 're not going to do that, that's where the Lyapunov theory comes in.

Lyapunov theory gives you a nice elegant mathematical tool to prove these

properties without having to actually solve it.

because otherwise, in a complicated system you have to look at the full response.

In the spring mass system, I can get an analytic answer,

what those oscillations are going to be and

you can come up with bounding functions, and for every epsilon I can do this.

But for nonlinear systems, an attitude problem,

you're not going to find analytic answers.

So those approaches can be very difficult.

So, this is just a definition, boundedness.

Here we have stability but not convergence, right?

On the linear system,

stability would imply convergence not with a non-linear system.

探索我们的目录

免费加入并获得个性化推荐、更新和优惠。

Coursera 致力于普及全世界最好的教育，它与全球一流大学和机构合作提供在线课程。

© 2019 Coursera Inc. 保留所有权利。

通过 App Store 下载

通过 Google Play 获取

Coursera
关于
管理团队
工作机会
目录
证书
MasterTrack™ 证书
学位
企业版
政府版

社区
Learners
Partners
Developers
Beta Testers
Translators

连接
博客
Facebook
领英
Twitter
Instagram
YouTube
技术博客

更多
条款
隐私
帮助
内容访问
媒体
联系我们
目录
附属公司