From b5fec8587e73fedb43368fdc4584f7ab3e1d4c07 Mon Sep 17 00:00:00 2001 From: Suiyi He Date: Sun, 17 Mar 2024 22:30:30 -0500 Subject: [PATCH] update readme (#11) --- README.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 2998baf..497bc71 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,19 @@ iterative-ilqr-tasks ========== -This repository provides a toolkit to test iterative learning controller. +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]](https://arxiv.org/abs/2302.14246): +If you find this project useful in your work, please consider citing following paper [[arXiv]](https://arxiv.org/abs/2302.14246) | [[IEEE]](https://ieeexplore.ieee.org/document/10383960): ``` @inproceedings{zeng2023i2lqr, - title={i2LQR: Iterative LQR for Iterative Tasks in Dynamic Environments}, - author={Yifan Zeng and Suiyi He and Han Hoang Nguyen and Yihan Li and Zhongyu Li and Koushil Sreenath and Jun Zeng}, - booktitle={2023 62nd IEEE Conference on Decision and Control (CDC)}, - year={2023}, + 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} } ``` @@ -28,7 +31,7 @@ pip install -e . ## Auto Testing -In this project, `pytest` is used to test the code autonomously after pushing new code to the repository. Currently, three files in the `tests` folder are used for testing pid or mpc tracking controller, mpc-cbf controller and racing game planner, respectively. To test other features, add files to the `tests` folder and update the `tests.yml` file under the `.github/workflows` folder. +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. @@ -73,9 +76,6 @@ This allows to test the iterative ilqr controller. The argparse arguments are li | `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 horizon and number of history states used for learning should be changed. -- No noise is added to the simulation during the dynamics update. The noise will result in failure when the robotics approaches the terminal point. -- Current discretization time for system dynamics update is same as the simulation timestep. A smaller this value will also result in failure. - - - +- 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.