Solve State Space Model HW: Find A, B, C, q

In summary, the conversation discusses a homework problem in which two systems are connected in cascade and the equivalent state space parameters need to be determined. The systems are represented by equations and the goal is to find q(t), A(t), B(t), and C(t) for the combined system. The speaker suggests that q(t) may be equal to [q1(t) q2(t)], A(t) to [A1(t) 0; B1(t) A2(t)], B(t) to [B1(t) 0; 0 B2(t)], and C(t) to [C1(t) 0; 0 C2(t)], but is unsure if this is correct. Assistance is requested.
  • #1
kev08106
4
0
Hi I have a problem I need to solve for a class soon.
The prof gave a us a homework problem where he gives us a state space representation for one system and connect it in cascade to another system and need the equivalent state space parameters:
Heres the question:
x(t)----> |system1 | --> y(t) --> |system2 |----> z(t)

system 1 is defined by:
dq1(t)/dt = A1(t)q1(t) + B1(t)x(t)
y(t) = C1(t)q1(t)

system 2 is defined by:
dq2(t)/dt = A2(t)q2(t) + B2(2)y(t)
z(t) = C2(t)q2(t)

the combination of the system is defined:
dq(t)/dt = A(t)q(t) + B(t)x(t)
z(t) = C(t)q(t)

It says find q(t), A(t), B(t), and C(t) in similar quantities for the separate systems.
 
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  • #2
I was thinking that q(t) = [q1(t) q2(t)] and then A(t) = [A1(t) 0; B1(t) A2(t)], B(t) = [B1(t) 0; 0 B2(t)], and C(t) = [C1(t) 0; 0 C2(t)] but I am not sure if this is correct. Any help would be appreciated. Thank you.
 
  • #3


To solve this problem, we first need to understand the concept of a state space model. A state space model is a mathematical representation of a physical system that describes the relationship between the input, output, and internal state of the system. In this case, the input is x(t), the output is z(t), and the internal state is represented by q(t).

To find the state space parameters for the combined system, we need to combine the equations for system 1 and system 2. This can be done by substituting the equation for y(t) from system 1 into the equation for dq2(t)/dt in system 2, giving us:

dq2(t)/dt = A2(t)q2(t) + B2(2)C1(t)q1(t)

Next, we can substitute the equation for q1(t) from system 1 into the equation for dq2(t)/dt, giving us:

dq2(t)/dt = A2(t)q2(t) + B2(2)C1(t)(A1(t)q1(t) + B1(t)x(t))

We can then simplify this equation by factoring out q1(t) and x(t), giving us:

dq2(t)/dt = (A2(t) + B2(2)C1(t)A1(t))q1(t) + B2(2)C1(t)B1(t)x(t)

This equation now has the same form as the equations for system 1 and system 2, with A(t) = A2(t) + B2(2)C1(t)A1(t), B(t) = B2(2)C1(t)B1(t), and C(t) = C2(t). Therefore, to find the state space parameters for the combined system, we can simply substitute these values into the equations for system 1 and system 2.

To find q(t), we can use the equation for q1(t) from system 1, which is:

q1(t) = e^(∫A1(t)dt)q1(0) + ∫e^(∫A1(t)dt)B1(t)x(t)dt

We can then substitute this equation into the equation for q2(t) from system 2, giving us:

q2(t) = e^(∫A2(t)dt)q2(0
 

1. What is a state space model?

A state space model is a mathematical representation of a system that describes how the system evolves over time. It consists of a set of variables known as state variables, which represent the internal state of the system, and a set of equations that describe how the state variables change over time.

2. How do you find the matrices A, B, and C in a state space model?

The matrix A represents the dynamics of the state variables, while the matrix B represents the influence of the input variables on the state variables. The matrix C relates the state variables to the output variables. These matrices can be found by using system identification techniques, such as least squares or maximum likelihood estimation, on data collected from the system.

3. What is the significance of the matrix q in a state space model?

The matrix q, also known as the process noise covariance matrix, represents the uncertainty or randomness in the system. It is used to model any disturbances or external factors that may affect the system but are not explicitly included in the state space model. q is typically estimated using statistical methods and can have a significant impact on the accuracy of the model predictions.

4. Can a state space model be used for nonlinear systems?

Yes, a state space model can be used for nonlinear systems by using nonlinear state and output equations. However, this can make the model more complex and difficult to solve, so linearization techniques are often used to approximate the nonlinear equations with linear ones. Another approach is to use a technique called the extended Kalman filter, which can handle both linear and nonlinear state space models.

5. What are some applications of state space models?

State space models have a wide range of applications in various fields such as control systems, economics, finance, and engineering. They are commonly used for modeling and predicting the behavior of complex systems, such as physical systems, financial markets, and biological systems. They are also used in the design and control of feedback systems, such as robots and process control systems.

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