Autocovariance Least Squares with Constrained Noise Covariance Model Identification

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The scripts to run the constrained Autocovariance Least Squares technique for two examples are contained in this repository.

Mass Spring Damper Example

1. gen_data_msd
   • Creates simulated datasets of the msd dynamics with process and measurement noise
   • Generates ALS inputs with initial suboptimal process noise covariance, $Q$ and measurement noise covariance, $R$.
2. run_als_msd
   • Run script for constrained ALS problem
   • Calls setup_ALS_msd.m with defines lags and other ALS inputs
   • als_msd
     • ALS class with mass spring damper constraints for 7 temperature problem
3. plot_lags_msd
   • Plots results from constrained ALS problem
   • Saves mean and standard deviation of $Q$ and $R$ solutions
   • Figure 2
4. plot_QR_T
   • Figure 5
5. Phi_f: Figure 3

Model for Aeroelastic Response to Gust Excitation

1. wtData_setup
   • Loads wind tunnel datasets:
   • Relies on models located here:
   • Generates ALS inputs with initial suboptimal process noise covariance, $Q$ and measurement noise covariance, $R$.
2. run_als_MARGE
   • Run script for constrained ALS problem
   • Calls setup_ALS_MARGE.m with defines lags and other ALS inputs
   • als_MARGE
     • ALS class with MARGE constraints for 5 dynamic pressure problem
3. plot_lags_MARGE
   • Plots results from constrained ALS problem
   • Saves mean and standard deviation of $Q$ and $R$ solutions
4. plot_QR_qbar
   • Figure 6
   • Relies on unconstrained ALS solutions:d
5. wtData_ALSest
   • Figures 7-10
   • Relies on wind tunnel data: