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Stir Tank

A stir tank reactor.

MPC ingredients

To compute the MPC ingredients, run the offlinempcingredients.m file in MATLAB. The output of the MATLAB file are already available in the folder mpc_parameters.

matlab -nodisplay -r "run('offlinempcingredients.m')"

The MPC parameters should be saved in human readable .txt form in the folder mpc_parameters.

MPC dataset generation

To generate samples of the MPC, call

python3 samplempc.py sample_mpc \\
    --numberofsamples=10

The results of this would be saved in a folder called datasets/stirtank_N_{numberofsamples}_{date}-{time}.

You can similarly create a larger dataset, by calling this function in parallel

python3 samplempc.py parallel_sample_mpc \\
    --instances=24 \\
    --samplesperinstance=10 \\
    --prefix=Cluster_test

The results of this would be saved in a folder called datasets/stirtank_N_{instances*samplesperinstance}_merged_{prefix}_{date}-{time}

If you downloaded the precomputed dataset for this example, you should find it under datasets/stirtank_N_960000.

Training a NN

If you want to train an approximator on the precomputed dataset for this example, call

python3 approximatempc.py find_approximate_mpc \\
    --dataset=stirtank_N_960000

The models will be saved in a models folder.

If you downloaded the pretrained NN, you should find it under models/10-200-400-600-600-400-200-30_mu=0.12_20230104-232806

Testing the NN

You can run closed loop test with the model calling

python3 safeonlineevaluation.py closed_loop_test_on_dataset \\
    --dataset=stirtank_N_240000_test \\
    --model_name=2-50-50-10_mu=1.00_20230106-170148 \\
    --N_samples=50000 \\
    --N_sim=1000