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Robust Multi-Stage Nonlinear Model Predictive Control Using Sigma Points
| Content Provider | MDPI |
|---|---|
| Author | Thangavel, Sakthi Paulen, Radoslav Engell, Sebastian |
| Copyright Year | 2020 |
| Description | We address the question of how to reduce the inevitable loss of performance that is incurred by robust multi-stage NMPC due to the lack of knowledge compared to the case where the exact plant model (no uncertainty) is available. Multi-stage NMPC in the usual setting over-approximates a continuous parametric uncertainty set by a box and includes the corners of the box and the center point into the scenario tree. If the uncertainty set is not a box, this augments the uncertainty set and results in a performance loss. In this paper, we propose to mitigate this problem by two different approaches where the scenario tree of the multi-stage NMPC is built using sigma points. The chosen sigma points help to capture the true mean and covariance of the uncertainty set more precisely. The~first method computes a box over-approximation of the reachable set of the system states whereas the second method computes a box over-approximation of the reachable set of the constraint function using the unscented transformation. The advantages of the proposed schemes over the traditional multi-stage NMPC are demonstrated using simulation studies of a simple semi-batch reactor and a more complex industrial semi-batch polymerization reactor benchmark example |
| Starting Page | 851 |
| e-ISSN | 22279717 |
| DOI | 10.3390/pr8070851 |
| Journal | Processes |
| Issue Number | 7 |
| Volume Number | 8 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-07-16 |
| Access Restriction | Open |
| Subject Keyword | Processes Automotive Engineering Adaptive Control Economic Model Predictive Control Multi-stage Decision Making Robust~model Predictive Control Parameter Estimation Parameter Uncertainty Unscented~transformation |
| Content Type | Text |
| Resource Type | Article |
