Finding the state space model of a nonlinear system

In summary, the conversation discusses the state space model of a nonlinear system and the process of linearizing it at a known equilibrium point. The Jacobian matrix is used to obtain the linearized state space model, and the output can be found by evaluating the Jacobian matrix at the specified equilibrium point.
  • #1
asd1249jf

Homework Statement


The state space model of a nonlinear system is
[tex]x'_1(t) = 2x^2_2(t) - 50[/tex]
[tex]x'_2(t) = -x_1(t) - 3x_2(t) + u(t)[/tex]

Where [tex]x_1(t)[/tex] and [tex]x_2(t)[/tex] are the states, and u(t) is the input. The output of the system is [tex]x_2(t)[/tex].

Find the state space model of this system linearized at the equilibrium point (-15, 5)

Homework Equations



State space modeling, Jacobian Matrix(?)

The Attempt at a Solution



I think my attempt lies on false assumption, but here's what I did.

Since we're linearizing at a known equilibrium point for a nonlinear system, and since [tex]x'_1(t) = 2x^2_2(t) - 50[/tex] contains non linear term, we must first take a derivative of it as denoted in Jacobian matrix. derivative of x_1(t) is

[tex]x''_1(t) = 4x_2(t)[/tex]

So the state space model we get is

[x'_1] = [ 0 4 ] [x_1] + [ 0 ] u(t)
[x'_2]aa[-1 -3 ] [x_2] a[ 1 ]

(Sorry I have no clue how to put this matrix in Latex).

But then how do we get the output from this state space model?
 
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  • #2
The linearization about (-15, 5) is obtained by evaluating the Jacobian matrix at (-15,5). Here the Jacobian matrix is [tex]
J(x_1,x_2) = \begin{pmatrix} 0 & 4x_2 \\ -1 & -3 \end{pmatrix}
[/tex] and the latex code is
Code:
J(x_1,x_2) = \begin{pmatrix} 0 & 4x_2 \\ -1 & -3 \end{pmatrix}
 
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  • #3
pasmith said:
The linearization about (-15, 5) is obtained by evaluating the Jacobian matrix at (-15,5). Here the Jacobian matrix is [tex]
J(x_1,x_2) = \begin{pmatrix} 0 & 4x_2 \\ -1 & -3 \end{pmatrix}
[/tex] and the latex code is
Code:
J(x_1,x_2) = \begin{pmatrix} 0 & 4x_2 \\ -1 & -3 \end{pmatrix}
So the evaluation at (-15,5) becomes

[tex]y'= \begin{pmatrix} 0 & 20 \\ -1 & -3 \end{pmatrix}[/tex]

?
 
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FAQ: Finding the state space model of a nonlinear system

1. How is the state space model different from other models?

The state space model is a mathematical representation of a system that describes the relationship between input, output, and internal states. Unlike other models, such as transfer function models, it can handle nonlinear systems and is more suitable for control system analysis and design.

2. How is the state space model of a nonlinear system determined?

The state space model of a nonlinear system is determined by using the system's equations of motion, which describe the dynamics of the system. These equations are then linearized around a set of operating points, resulting in a set of linearized state space equations. This process is also known as linearization.

3. Can a nonlinear system have a unique state space model?

No, a nonlinear system can have multiple state space models depending on the chosen operating points for linearization. Different sets of operating points can result in different linearized equations, leading to different state space models. However, all of these models will still describe the same system.

4. What are the advantages of using the state space model for nonlinear systems?

The state space model allows for a more accurate representation of nonlinear systems compared to other models. It also provides more flexibility in control system design and analysis, as it can handle systems with multiple inputs and outputs. Additionally, it is easier to incorporate disturbances and uncertainties into the state space model.

5. Can the state space model of a nonlinear system be used for simulation and control?

Yes, the state space model of a nonlinear system can be used for simulation and control. It can be used to simulate the system's response to various inputs and disturbances, as well as design controllers to regulate its behavior. However, the state space model should only be used within the operating range for which it was linearized.

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