Joint State and Parameter Estimation for Discrete-Time Polytopic Linear Parameter-Varying Systems*. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Beelen, H.P.G.J.; Donkers, M.C.F.
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 9778
• Page End: 9783
• Publication Date: 2017-07
• Subjects: State estimation -- parameter estimation -- observer design -- polytopic systems -- LPV; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.880
• Languages: English
• ISSNs: 2405-8963

Abstract

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.