This code explains the implementation of distributed minimax adaptive control algorithm
## Associated Paper
V. Renganathan, Anders Rantzer and Olle Kjellqvist, ``Disributed 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 ACC-LCSS 2024.
## Dependencies
Please download and include the source files of package that converts MATLAB to tikz into the path.
The package can be found in https://www.mathworks.com/matlabcentral/fileexchange/22022-matlab2tikz-matlab2tikz
## Steps to run the simulation
The main file to be run in MATLAB is the ``DisributedMinimaxAdaptiveControl.m'' file. Please follow the following steps in running the file.
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
- 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
