Basic classical control theory-related projects.
Execute the following command in the main directory.
export PYTHONPATH=$PWD
python=3.8.12
numpy=1.21.2
tensorflow=2.7.0
matplotlib=3.5.1
control=0.9.1
gym=0.21.0
box2d-py=2.3.8
cma=3.2.2
scipy=1.8.0
- Control system modeled using gradient descent on
$\dot x = Ax + Bu$ - Cartpole control solver
- Manual eigenvalue placement
- Search
$T = A - BK$ that maximizes the return using CMA-ES - Continuous LQR
- Linear system classification analysis for
$T$ - Demo:
python run/cartpole.py
https://github.com/johnlime/ik_lqr/tree/master/run/continuous_cartpole_dynamics
- Control system modeled using gradient descent on
$x[t+1] = Ax[t] + Bu[t]$ - Cartpole control solver
- Manual eigenvalue placement
- Search
$T = A - BK$ that maximizes the return using CMA-ES - Continuous and discrete LQR
- Linear system classification analysis for
$T$
https://github.com/johnlime/ik_lqr/tree/master/run/discrete_cartpole_dynamics
- Uses the "pigeon" environment
- Jacobian matrix calculated manually.
- Demo:
python run/pigeon_ik.py