Abstract
In this paper, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We show that the moment based formulation yields predictive control problems which are computationally simpler to solve compared to the existing robust MPC formulations, while providing statistical robustness properties. We apply the proposed MPC technique to a simple simulation example to demonstrate its effectiveness.
| Original language | English |
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| Title of host publication | 2017 American Control Conference, ACC 2017, 24-26 May 2017, Seattle, Washington |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 3072-3077 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5090-5992-8 |
| ISBN (Print) | 978-1-5090-4583-9 |
| DOIs | |
| Publication status | Published - 29 Jun 2017 |
| Event | 2017 American Control Conference (ACC 2017) - Sheraton Seattle Hotel, Seattle, United States Duration: 24 May 2017 → 26 May 2017 http://acc2017.a2c2.org/ |
Conference
| Conference | 2017 American Control Conference (ACC 2017) |
|---|---|
| Abbreviated title | ACC 2017 |
| Country/Territory | United States |
| City | Seattle |
| Period | 24/05/17 → 26/05/17 |
| Internet address |