Abstract
Stabilizing conditions for nonlinear predictive control typically rely on standard Lyapunov functions and thus require a monotonically decreasing cost function. These conditions cannot certify stability of predictive controllers in the presence of non–monotonic cost functions. In this paper we develop new dissipativity–based stabilizing conditions for nonlinear predictive control that allow for non–monotonic cost functions. Firstly, we establish that dissipation inequalities with a cyclically negative supply imply asymptotic stability. Secondly, we show that closed–loop trajectories generated by predictive control satisfy a fundamental dissipation inequality. This enables dissipativity–based stabilizing conditions that do not require a special terminal cost and apply to both model–based and data–driven predictive control algorithms.
| Original language | English |
|---|---|
| Pages (from-to) | 159-165 |
| Number of pages | 7 |
| Journal | IFAC-PapersOnLine |
| Volume | 54 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Jul 2021 |
| Event | 7th IFAC Conference on Nonlinear Model Predictive Control, NMPC 2021 - Bratislava, Slovakia Duration: 11 Jul 2021 → 14 Jul 2021 |
Bibliographical note
Publisher Copyright:Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)
Keywords
- Data–driven control
- Dissipative systems
- Lyapunov function
- Predictive control
- Stability of nonlinear systems