State-space modeling of a control system

In summary, the problem involves creating a vector and multiplying it by a matrix in order to determine the forward path. The problem also defines three states (applied voltage, angular velocity, and position) and requires the use of the derivative of one of these states. The resulting equation is in the form of x'=Ax + Bu, where A is a 3x3 matrix and B is a vector directly affected by the input. The second row of A is determined by the derivative of θ_m.
  • #1
yaro99
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0

Homework Statement


[/B]
Here is the problem:
8w3L1VQ.png


And this is the system in question:

atrLdna.png


Homework Equations


G(s) = θ_0(s)/θ_i(s) = output/input (open-loop transfer function)

The Attempt at a Solution



I've been staring at this problem and looking through my textbook for over an hour but honestly I'm not sure how to begin here. I think I'm supposed to create a vector [x1, x2, x3] and multiply it by a matrix in order to get the forward path. I'm still confused on how to determine this matrix, as well as what to do about x2, since the problem defines it as angular velocity ω_m, and I don't see that anywhere on the diagram (I'm assuming it's the derivative of θ_m?).

From looking at my notes, I believe I'm supposed to have something in the form of x'=Ax + Bu (A and B are matrices, x and u are vectors). The only problems of this kind that I've seen in class were circuit problems, but that doesn't help me here.

I'm not too worried about the MATLAB part, if I can solve the first part I believe I can figure it out.
 
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  • #2
For 2.2 A,

you want to have 3 states
1.applied voltage E(a)
2.velocity ω_m
3. position θ_m

just name E(a)=X1
ω_m=X2
θ_m=X3

so X=[E(a) w_m θ_m]'

X'=AX+BU
A will have to be a 3x3 matrix,

yaro99 said:
(I'm assuming it's the derivative of θ_m?).
you assumed correct... knowing that what is the second row of A?
also B should be fairly easy (hint: there is only one state that will be directly affected by the input
 

1. What is state-space modeling of a control system?

State-space modeling of a control system is a mathematical approach used to describe the behavior of a dynamic system over time. It involves representing the system in terms of its internal state variables, inputs, and outputs, and using differential equations to model their relationships. This method is commonly used in control engineering to design and analyze feedback control systems.

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

State-space modeling is different from other techniques, such as transfer function models, in that it considers the internal state variables of a system, rather than just its inputs and outputs. This allows for a more comprehensive and accurate representation of the system's behavior and enables the design of more complex control systems.

3. What are the benefits of using state-space modeling in control systems?

There are several benefits to using state-space modeling in control systems. First, it allows for the design and analysis of more complex control systems with multiple inputs and outputs. Additionally, it can handle systems with nonlinear dynamics, making it more versatile than other modeling techniques. State-space models are also easier to analyze and simulate, making them useful for both theoretical and practical applications.

4. What are the limitations of state-space modeling?

Like any modeling technique, state-space modeling has its limitations. One limitation is that it requires accurate and complete knowledge of the system's dynamics, which may be difficult to obtain in some cases. Additionally, state-space models can be computationally intensive, making them more challenging to implement in real-time control systems. In some cases, simpler modeling techniques may be more suitable.

5. How can state-space modeling be used in real-world applications?

State-space modeling has various real-world applications, particularly in the fields of control engineering and systems biology. It is used to design and analyze control systems, such as in automotive and aerospace industries. It is also used in modeling and controlling biological systems, such as metabolic pathways in cells. Additionally, state-space models can be applied in economics, finance, and other fields to study dynamic systems.

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