How to Analyze Root Locus in Matlab for DC Motor Control?

In summary, the conversation discusses attempting to analyze the root locus of a transfer function in Matlab to control a DC motor. The initial root locus drawn looks odd and unstable at all gains, but adding a zero in rltool results in a stable system. The system is confirmed to be stable for all gains and further adjustments can be made to improve its speed while maintaining stability.
  • #1
eh87
8
0

Homework Statement


I'm trying to analyze the root locus of this transfer function in Matlab to control a DC motor:

mysys=tf([0.022],[0.00000000007 0.0000000626 0.000493])


The Attempt at a Solution



Using:

%[Kp,P]=rlocfind(mysys)
%rltool(mysys)
rlocus(mysys)

I get a root locus that looks somewhat odd and I'm not really sure how to interpret it. It doesn't appear to be stable at all gains. Although, it does look a bit better when I add a zero in rltool to the right of that gap and then it seems stable at all gains.

Help? I'm lost.
 
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  • #2
Your system has two poles in the LHP, so it is stable for all values of gain. Matlab should draw a root locus consisting of two vertical lines, starting in each of the two complex conjugate poles.
Since no part of the root locus goes to the RHP, the system is stable.
Can you please post the image of the RL?
 
  • #3
Thanks for the reply :)

Yes, they are in the LHP but I don't think it's stable for all gains. I know that as long as they don't cross into the RHP it should be, but this is just confusing. Please see the attached picture.

In rltool, when I move the pink dots (gain) it seems like there's a limit that they won't cross, which makes me think this is not stable at high gains (?). I'm not sure really what it means, but that's my guess.
 

Attachments

  • ece456 root locus.jpg
    ece456 root locus.jpg
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  • #4
eh87 said:
Thanks for the reply :)

Yes, they are in the LHP but I don't think it's stable for all gains. I know that as long as they don't cross into the RHP it should be, but this is just confusing. Please see the attached picture.

In rltool, when I move the pink dots (gain) it seems like there's a limit that they won't cross, which makes me think this is not stable at high gains (?). I'm not sure really what it means, but that's my guess.

I can't see your picture, because the authorization is pending. Anyway, the system is certainly stable for all gains.
I have tried to adjust the loop gain in rltool and reached 1.02e6, without problem, I stopped there, but could continue. For practical purposes this is infinity.
 
  • #5
Hmm. Maybe I did something wrong before.

Anyway, I checked on the response to a step function in rltool today and it seemed fine. I guess now I'll work on tweaking it to make it faster without losing stability, then re-post.

Thanks!
 

1. What is Matlab Root Locus Analysis?

Matlab Root Locus Analysis is a tool used for analyzing the stability and performance of control systems. It helps in understanding how the system's poles and zeros change as the system parameters are varied.

2. How does Matlab Root Locus Analysis work?

Matlab Root Locus Analysis uses a graphical method to plot the locations of the closed-loop poles of a system as a function of a varying parameter. By analyzing this plot, one can determine the stability and performance of the system.

3. What are the benefits of using Matlab Root Locus Analysis?

Matlab Root Locus Analysis provides a visual representation of the system's behavior, making it easier to understand and analyze. It also allows for quick changes in system parameters and observation of their effects on the system's performance.

4. What types of systems can be analyzed using Matlab Root Locus Analysis?

Matlab Root Locus Analysis can be used to analyze both open-loop and closed-loop control systems with single-input single-output (SISO) or multiple-input multiple-output (MIMO) configurations.

5. Are there any limitations to using Matlab Root Locus Analysis?

Matlab Root Locus Analysis is based on linear systems, so it may not accurately represent the behavior of nonlinear systems. It also assumes that the system parameters are constant, which may not always be the case in real-world systems.

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