Homothetic tube Model Predictive Control with multi-step predictors

This GitHub repository contains the accompanying code for the paper titled "Homothetic Tube Model Predictive Control with Multi-Step Predictors." The code is implemented in MATLAB and includes two main files:

Main Files:

1. main_offlineComputation.m: This script takes the identified parameters and the dataset contained in the file "IdentifiedModelandData.mat" as inputs. It is responsible for generating all the offline computed sets and parameters needed for the MPC (Model Predictive Control) approach described in the paper. Specifically, it computes the set 𝒳₀, the feedback gain K, matrix P, and all other offline quantities defined in equations (10)-(12). These computed quantities are saved in a file named "offlineComputation.mat" for later use.

2. main_HomoMPC_multistep.m: This script serves as the main controller. It takes as input the data stored in "offlineComputation.mat" and simulates the system by applying the control law generated by solving the MPC problem addressed in the paper.

Dependences:

1. Yalmip

2. The Multi-Parametric Toolbox (MPT3)

Getting Started:

To use this code, follow these steps:

1. Clone this repository to your local machine using the following command:

git clone https://github.com/DecodEPFL/HomotheticMPCmultistep.git

2. Ensure you have MATLAB installed on your system.

3. Open MATLAB and navigate to the cloned repository's directory.

4. Run main_offlineComputation.m to compute the offline quantities and save them to "offlineComputation.mat".

5. After the offline computations are completed, you can run main_HomoMPC_multistep.m to simulate the system using the MPC controller.

Note:

• Make sure to have the required datasets and identified parameters in the appropriate format as mentioned in the paper.

• The code provided here is a companion to the paper and should be used in conjunction with the paper's instructions and explanations for a comprehensive understanding of the algorithm and methodology.

• Feel free to reach out Danilo Saccani ([email protected]) or Johannes Köhler ([email protected]) if you have any questions.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Citation

If you use this code in your research, please cite the accompanying paper:

Saccani, D., Ferrari-Trecate, G., Zeilinger, M. N. and Köhler, J. (2023). Homothetic tube model predictive control with multi-step predictors. IEEE Control Systems Letters.