Abstract
Modeling is recognized to be one of the toughest and most time-consuming tasks in modern nonlinear control engineering applications. Linear parameter-varying (LPV) models deal with such complex problems in an effective way, by exploiting well-established tools for linear systems while, at the same time, being able to accurately describe highly nonlinear and time-varying plants. When LPV models are derived from experimental data, it is difficult to estimate a priori how modeling errors will affect the closed-loop performance. In this work, a method is proposed to directly map data onto LPV controllers. Specifically, a hierarchical structure is proposed both to maximize the system performance and to handle signal constraints. The effectiveness of the approach is illustrated via suitable simulation tests.
| Original language | English |
|---|---|
| Title of host publication | Data-Driven Modeling, Filtering and Control |
| Editors | Carlo Novara , Simone Formentin |
| Publisher | Institution of Engineering and Technology |
| Chapter | 11 |
| Pages | 213-237 |
| Number of pages | 25 |
| ISBN (Electronic) | 9781785617133 |
| ISBN (Print) | 9781785617126 |
| DOIs | |
| Publication status | Published - Jul 2019 |
Keywords
- Closed loop systems
- Constrained systems
- Control system analysis and synthesis methods
- Control system synthesis
- Data-driven LPV control design
- Hierarchical structure
- Linear closed-loop performance
- Linear parameter varying systems
- Linear parameter-varying models
- Nonlinear control engineering
- Nonlinear control systems
- Time-consuming tasks