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optimal_quad_control_SL

End-to-end optimal quadcopter control through Supervised Learning

Notebooks

  1. Data Generation: used to generate the datasets of optimal trajectories
  2. Network Training: uses the generated datasets to train a G&CNet to learn the optimal state feedback
  3. Simulation: simulates the quadcopter controlled by a trained G&CNet
  4. Generate C code: used to convert the G&CNet from pytorch to c code
  5. Minimum Snap trajectories: used to compute the minimum snap polynomial trajectories and convert it into C code

Note that in order to run the Dataset Generation notebook, AMPL (A Mathematical Programming Language) needs to be installed as well as the NLP solver SNOPT.