Control Systems: Uncertainties w/ State feedback & Observer controller

In summary, the conversation discusses a continuous time state space model governed by matrices A, B, C, and D. The goal is to find the loop gain symbolically using MATLAB and to combine the observer and state feedback to simulate the system for a step input and non-zero initial conditions. The conversation also mentions designing the observer and feedback matrix for given poles, with the resulting gains being K = [7.0000 12.0000] and L = [-44.5000 -55.5000]. The individual's attempt at a solution involves converting the state space model into a second-order transfer function and using the closed-loop transfer function to determine the gain. However, there is uncertainty about how to determine the gain from the transfer
  • #1
jumpboy
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0

Homework Statement


Consider the continuous time state space model governed by:

A = [-1 0;1 0]
B = [1; 0]
C = [1 -1]
D = 0

a) suppose we are using the following state feedback and observer gains

K = [k1 k2]
L = [l1;l2]

find the loop gain symbolically using MATLAB (aka find process & controller gains)

b) combine the observer and the state feedback and simulate the system for a step input and non-zero initial conditions on x_tilda

Homework Equations



Other parts of this question that I omitted called for designing the observer & feedback matrix for given poles. The result is:

K = [7.0000 12.0000]
L = [-44.5000; -55.5000]

For part a) i used the following block diagram from my textbook and course in trying to figure out the controller and process gains:

[PLAIN]http://img.photobucket.com/albums/v68/jumpboyb/Screenshot2010-12-08at21409PM.png

The Attempt at a Solution



for part a) I turned the state space model into a second-order transfer function:

P(s) = (s - 1)/(s^2 + s)

I believe I blanked on turning the controller portion into a transfer function because I could not derive C(s).

With P(s) and C(s) the closed-loop transfer function would be defined by:

G(s) = P(s)C(s) / (1 + P(s)C(s))

From G(s), I am unsure how to determine the gain. Would I examine the maximum of the bode plot?


for b) the x_tilda value is what goes into the A block in the controller so I just need to look at that value. Implementing this simulation in MATLAB is where I am having issues.
 
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  • #2
I am not sure how to set up the state-space model for the controller or the observer.Thank you in advance for any advice/help
 

1. What is the purpose of using state feedback in control systems?

State feedback is used in control systems to regulate the behavior of a system by manipulating its state variables. This allows for better control over the system's response to external inputs, disturbances, and uncertainties.

2. How does state feedback handle uncertainties in a control system?

State feedback uses a feedback loop to continuously monitor the system's state variables and adjust the control inputs accordingly. This allows for the system to adapt to uncertainties and maintain stability and performance.

3. What is an observer controller in a control system?

An observer controller is a component in a control system that estimates the system's internal states based on its inputs and outputs. This estimation is used to provide feedback to the state feedback controller, allowing for more accurate control even when not all state variables are directly measurable.

4. How do uncertainties affect the performance of a control system with state feedback and an observer controller?

Uncertainties can affect the performance of a control system with state feedback and an observer controller by introducing errors in the estimated states. These errors can lead to suboptimal control and potentially destabilize the system if not properly accounted for.

5. What strategies can be used to mitigate the impact of uncertainties in control systems?

There are several strategies that can be used to mitigate the impact of uncertainties in control systems, such as robust control techniques, incorporating adaptive control algorithms, and using redundancy in the system design. Additionally, system identification and calibration can also help to reduce uncertainties and improve the accuracy of state estimation.

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