06 - Kalman Filters and LQR

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Electrical Engineering

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Jan 9, 2024

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State Estimators and LQR Part 1: Kalman Filters a) Create a state space model for the below system. The constants for the above system are as follows: k 1 = 0.5 k 2 = 0.3 m = 7 b = 0.2
State Estimators and LQR b) Load the model in Simulink and test the closed loop displacement response to a step input.
State Estimators and LQR c) Add noise sources W and V to the system as below. Using a noise power of 0.02 and a sample time of 0.01. Show your results.
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State Estimators and LQR d) Add a Kalman filter to this system and attempt to remove the additional noise. Hint: remember to switch the system to continuous time! Compare the output (yhat) of the Kalman filter to the now noisy data. How close is that to the clean system? The clean system and the Kalman filter are very similar in terms of output meaning that the Kalman filter can estimate the true state of the system’s noisy measurements. This causes the system’s estimate response to be overall much cleaner. Part 2: LQR. a) Develop the gains K needed to drive the above system with a prioritization of controlling displacement with minimal acceleration. Provide both your system response and values for K. Note: Remember that you’ll need to modify the structure of some of your signals here! After modifying the gain value, we produced a K value of 1.0e+04 which results in the minimal accelerations of 0.0112, 1.4491, and 2.7785.