iterative-ilqr-tasks

This repository provides a toolkit to test a novel control strategy called Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR). The strategy aims to improve closed-loop performance with local trajectory optimization for iterative tasks in a dynamic environment.

References

If you find this project useful in your work, please consider citing following paper [arXiv] | [IEEE]:

@inproceedings{zeng2023i2lqr,
  title={i2LQR: Iterative LQR for Iterative Tasks in Dynamic Environments},
  author={Zeng, Yifan and He, Suiyi and Nguyen, Han Hoang and Li, Yihan and Li, Zhongyu and Sreenath, Koushil and Zeng, Jun},
  booktitle={2023 62nd IEEE Conference on Decision and Control (CDC)},
  pages={5255--5260},
  year={2023},
  organization={IEEE}
}

Installation

• We recommend creating a new conda environment:

conda env create -f environment.yml
conda activate iterative-ilqr

Run following command in terminal to install the iterative-ilqr package.

pip install -e .

Auto Testing

In this project, pytest is used to test the code autonomously after pushing new code to the repository. Currently, files in the tests folder are used for testing i2LQR controller and nonlinear learning based MPC (NLMPC) controller, respectively. To test other features, add files to the tests folder and update the tests.yml file under the .github/workflows folder.

Contributing

Execute pre-commit install to install git hooks in your .git/ directory, which allows auto-formatting if you are willing to contribute to this repository.

Please contact major contributors of this repository for additional information.

Quick-Demos

Docs

The following documentation contains documentation and common terminal commands for simulations and testing.

Nonlinear LMPC

Run

python iterative_ilqr/tests/nlmpc_test.py --lap-number 10 --num-ss-iters 2 --num-ss-points 8 --ss-option space

This allows to test the nonlinear lmpc controller. The argparse arguments are listed as follow,

name	type	choices	description
lap_number	int	any number that is greater than 2	number of laps that will be simulated
num_ss_iters	int	any number that is greater than 1	iterations used for learning
num_ss_points	int	any number that is greater than 1	history states used for learning
ss_option	string	space, time or all	criteria for history states selection
plotting	action	store_true	save plotting if true
save_trajectory	action	store_true	save simulator will store the history states and inputs if true

Iterative lqr for iterative tasks

Run

python iterative_ilqr/tests/ilqr_test.py --lap-number 10 --num-ss-iters 2 --num-ss-points 8

This allows to test the iterative ilqr controller. The argparse arguments are listed as follow,

name	type	choices	description
lap_number	int	any number that is greater than 2	number of laps that will be simulated
num_ss_iters	int	any number that is greater than 1	iterations used for learning
num_ss_points	int	any number that is greater than 1	history states used for learning
plotting	action	store_true	save plotting if true
save_trajectory	action	store_true	save simulator will store the history states and inputs if true

Known Issues

• To change the simulation timestep, the number of prediction horizons and the number of history states used for learning should be adjusted.
• No noise is added to the simulation during the dynamics update. The presence of noise may lead to failure when the robotics approaches the terminal point.
• The current discretization time for system dynamics update is the same as the simulation timestep. Decreasing this value may also result in failure.