Understanding State Space Representation for First-Order Dynamic Systems

In summary, the Homework group suggests taking the given dynamic system equation and representing it in terms of the y(t) variable instead of the x(t) variable. This may help to make the problem more clear.
  • #1
jegues
1,097
3

Homework Statement



A first-order dynamic system is represented by the differential equation,

[tex]5\frac{dx(t)}{dt} + x(t) = u(t).[/tex]

Find the corresponding transfer function and state space reprsentation.

Homework Equations





The Attempt at a Solution



Putting the equation in the Laplace domain yields,

[tex]5sX(s) + X(s) = U(s)[/tex]

[tex]\Rightarrow G(s) = \frac{X(s)}{U(s)} = \frac{1}{1+5s}[/tex]

For the state space equations,

[tex]\frac{dx(t)}{dt} = -0.2x(t) + 0.2u(t)[/tex]

The answer they provide is,

[tex]\frac{dx(t)}{dt} = -0.2x(t) + 0.5u(t), \quad y(t) = 0.4x(t)[/tex]

How did they 0.5u(t) and how did they know that y(t) = 0.4x(t)?

Thanks again!
 
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  • #2
Bump, can someone please clarify this for me?
 
  • #3
Hi jeques. I don't know if I can help to clarify your problem. The definition of the first-order system made no mention of y(t), so it's a mystery to me where it came from at the end! Is there something missing from the problem statement that might tie-in with y(t)?
 
  • #4
gneill said:
Hi jeques. I don't know if I can help to clarify your problem. The definition of the first-order system made no mention of y(t), so it's a mystery to me where it came from at the end! Is there something missing from the problem statement that might tie-in with y(t)?

Here's the question. (see attached)
 

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  • #5
Hmm. Nope, that doesn't help me :frown: The transfer function bit is clear enough, but I don't "get" the introduction of the y(t) stuff. I'll see if I can find someone who recognizes the problem type.
 
  • #6
Upon reflection and discussion with another Homework Helper, it occurred to me that the problem would make more sense to me if the variable used in the dynamic system differential equation was y rather than x.

Is it possible that we should take the given system D.E. to represent the form of the equation describing the system rather than an equation of the state variables?
 

1. What is state space representation?

State space representation is a mathematical model used to describe the behavior of a system over time. It represents the system as a set of variables, called states, and the relationships between these states. This model allows scientists to analyze and predict the behavior of complex systems.

2. How is state space representation different from other modeling techniques?

State space representation differs from other modeling techniques, such as differential equations or block diagrams, in that it explicitly includes all possible states of a system and their relationships. This allows for a more comprehensive analysis of the system's behavior and interactions.

3. What are the advantages of using state space representation?

State space representation has several advantages, including its ability to handle nonlinear systems, its flexibility in representing different types of systems, and its ability to incorporate time-varying parameters. It also allows for the use of advanced mathematical techniques, such as control theory and optimization, to analyze and design systems.

4. Can state space representation be applied to any type of system?

Yes, state space representation can be applied to a wide range of systems, including physical, biological, and social systems. It is particularly useful for complex systems with many interacting components, as it allows for a more comprehensive understanding of their behavior.

5. How is state space representation used in practical applications?

State space representation is used in a variety of practical applications, including control systems, robotics, economics, and epidemiology. It is also commonly used in engineering and scientific research to model and analyze complex systems. Additionally, state space representation is used in machine learning and artificial intelligence algorithms for pattern recognition and prediction.

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