Descripción
|
|
---|---|
In this article, we propose two event-based model predictive control (MPC) schemes with adaptive prediction horizon for tracking of unicycle robots with additive disturbances. The schemes are able to reduce the computational burden from two aspects: reducing the frequency of solving the optimization control problem (OCP) to relieve the computational load and decreasing the prediction horizon to decline the computational complexity. Event-triggering and self-triggering mechanisms are developed to activate the OCP solver aperiodically, and a prediction horizon update strategy is presented to decrease the dimension of the OCP in each step. The proposed schemes are tested on a networked platform to show their efficiency. | |
Internacional
|
Si |
JCR del ISI
|
Si |
Título de la revista
|
IEEE/ASME Transactions on Mechatronics |
ISSN
|
1083-4435 |
Factor de impacto JCR
|
|
Información de impacto
|
|
Volumen
|
|
DOI
|
10.1109/TMECH.2019.2962099 |
Número de revista
|
|
Desde la página
|
739 |
Hasta la página
|
749 |
Mes
|
DICIEMBRE |
Ranking
|
: Automation & Control Systems Q1 Engineering Electrical & Electronic Q1 |