This repository is dedicated to the implementation and study of Online Linear Quadratic Regulator (LQR), which combines traditional LQR with reinforcement learning techniques.

• MATLAB Files:

  • CTLQR_PI_3x3_Sogang.m: Continuous-time LQR with Proportional-Integral control.
  • DTLQR_Q_3x3_Sogang.m: Discrete-time LQR with state-space representation.
  • DTLQR_Q_3x3_Sogang_Nonlin_ref.m: Discrete-time LQR with nonlinear reference.
  • DTLQR_Q_3x3_Sogang_Nonlin_ref_PI.m: Discrete-time LQR with nonlinear reference and PI control.

• Python Files:

  • MPC_simple_pendulum.py: Model Predictive Control (MPC) implementation for a simple pendulum system.

• MATLAB (for running the .m files)
• Python 3.6 or higher (for running the .py files)
• Required Python libraries: numpy, scipy, matplotlib

1. Clone the repository:

   git clone https://github.com/mincasurong/Online-LQR.git
   cd Online-LQR

2. Install the required Python libraries:

   pip install numpy scipy matplotlib

Open the .m files in MATLAB and run them to see the results of various LQR implementations.

To run the Model Predictive Control example for a simple pendulum:

python MPC_simple_pendulum.py