Abstract
Linear parameter-varying systems are very suitable for modelling nonlinear systems, since well-established methods from the linear-systems domain can be applied. Knowledge about the scheduling parameter is an important condition in this modelling framework. In case this parameter is not known, joint state and parameter-estimation methods can be employed, e.g., using interacting multiple-model estimation methods, or using an extended Kalman filter. However, these methods cannot be directly used in case the parameters lie in a polytopic set. Furthermore, these existing methods require tuning in order to have convergence and stability. In this paper, we propose to solve the joint-estimation problem in a two-step, Dual Estimation approach, where we first solve the parameter-estimation problem by solving a constrained optimisation problem in a recursive manner and secondly, employ a robust polytopic observer design for state estimation. Simulations show that our novel method outperforms the existing joint-estimation methods and is a promising first step for further research.
Original language | English |
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Pages (from-to) | 9778-9783 |
Number of pages | 6 |
Journal | IFAC-PapersOnLine |
Volume | 50 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Jul 2017 |
Event | 20th World Congress of the International Federation of Automatic Control (IFAC 2017 World Congress) - Toulouse, France Duration: 9 Jul 2017 → 14 Jul 2017 Conference number: 20 https://www.ifac2017.org/ |
Keywords
- LPV
- observer design
- parameter estimation
- polytopic systems
- State estimation