diff --git a/README.md b/README.md index 6ffce4a8ef980a44d85fcc7fed5951abad4512d3..aea3fead4a3f74cf099661820ec66d0e25cb6a56 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,10 @@ # Distributed Minimax Adaptive Control ## Description -This code explains the implementation of distributed minimax adaptive control algorithm +This code explains the implementation of distributed minimax adaptive control algorithm designed for controlling uncertain networks ## Associated Paper -V. Renganathan, Anders Rantzer and Olle Kjellqvist, `Distributed Minimax Adaptive Control for Uncertain Networks`, Submitted to ACC-LCSS 2024. +V. Renganathan, Anders Rantzer and Olle Kjellqvist, `Distributed Minimax Adaptive Control for Uncertain Networks`, Submitted to European Control Conference 2024. ## Dependencies Please download and include the source files of package that converts MATLAB to tikz into the path. @@ -13,11 +13,10 @@ The package can be found in https://www.mathworks.com/matlabcentral/fileexchange ## Steps to run the simulation The main file to be run in MATLAB is the `DistributedMinimaxAdaptiveControl.m` file. Please follow the following steps in running the file. -- In line 31, set the `dataPrepFlag` to 1 if new network data has to be generated. Set it to 0, if you want to preload the already generated network dynamics data. -- In line 77, set the number of models to be prepared using the variable `numModels` -- In line 205, set the index of the true dynamics model using the variable `modelNum` -- In line 213, set the `disturbFlag`. Choose 0: none, 1: white noise, 2: sinusoid, 3: constant step -- +- Set the `dataPrepFlag` to 1 if new network data has to be generated. Set it to 0, if you want to preload the already generated network dynamics data. +- Set the number of models to be prepared using the variable `numModels` +- Set the index of the true dynamics model using the variable `modelNum` +- Set the `disturbFlag`. Choose 0: none, 1: white noise, 2: sinusoid, 3: constant step ## License MIT License: