Code for the Implementation of State-Dependent dynamic tube model predictive control

doi: 10.4121/82150c4b-eea2-46f4-8a47-fcb6bf3d8e3d.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/82150c4b-eea2-46f4-8a47-fcb6bf3d8e3d
Datacite citation style:
Surma, Filip (2023): Code for the Implementation of State-Dependent dynamic tube model predictive control . Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/82150c4b-eea2-46f4-8a47-fcb6bf3d8e3d.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software

The code of this repository is used to implement and test a new algorithm called SDD-TMPC. It is a control algorithm that sacrifices a bit of optimality (much less than tube MPC) but returns robust solutions. In this repository, MPC, TMPC, and SDD-TMPC were implemented.


SDD-TMPC needs to have a model of boundaries of future disturbance. I used a fuzzy logic-based model trained with a genetic algorithm.


4 scenarios were created to compare all methods:

1. Following a path in an empty trajectory

2. Moving closely to the wall

3. Moving through a narrow corridor.

4. Avoiding an obstacle


history
  • 2023-06-02 first online, published, posted
publisher
4TU.ResearchData
format
Matlab code, mat files, fig files
funding
  • Netherlands Organization for Scientific Research, 655.010.207
organizations
TU Delft, Faculty of Aerospace Engineering, Department of Control & Simulation

DATA

To access the source code, use the following command:

git clone https://data.4tu.nl/v3/datasets/9aefaae8-7eee-49e9-8545-88c67fac56fb.git
files (1)