| author | = | {B. Tak\'acs and J. {\v{S}}tevek and R. Valo and M. Kvasnica}, |
| title | = | {Python Code Generation for Explicit MPC in MPT}, |
| booktitle | = | {European Control Conference 2016}, |
| year | = | {2016}, |
| pages | = | {1328-1333}, |
| address | = | {Aalborg, Denmark}, |
| annote | = | {The paper shows how explicit representations of model predictive control (MPC) feedback laws can be embedded into Python applications via a new code-generation module of the Multi-Parametric Toolbox. The advantage of the explicit approach is that it provides a simple and fast computation of optimal control inputs without solving optimization problems on-line. To enable implementation of discontinuous feedback laws, the paper proposes an extended version of the sequential search algorithm which resolves possible multiplicities based on a secondary evaluation of the cost function. Two applications are considered. The first one is the Flappy Bird game where we design an MPC-based artificial player to control flapping of the bird’s wings. The second application considers the design and implementation of an explicit MPC controller for a quadrocopter.}, |
| url | = | {https://www.uiam.sk/assets/publication_info.php?id_pub=1737} |
}