Qin, S. Joe (Univ. of Texas), Ljung, Lennart (Linkoping Univ.)
On the Role of Future Horizon in Closed-Loop Subspace Identification
Scheduled for presentation during the Invited Session "New Developments in Closed-Loop Subspace Identification" (FrA3), Friday, March 31, 2006,
12:10−12:30, Hunter Room
14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia
This information is tentative and subject to change. Compiled on July 16, 2018
|Keywords Closed Loop Identification, Subspace Methods, Multivariable System Identification
Closed loop subspace identi»cation has become a focus of interest with several recent developments. Notably are the innovation estimation approach (Qin and Ljung, 2003), the state space approach with ARX pre-estimates (SSARX, (Jansson, 2003)), and the whitening »lter approach (Chiuso and Picci, 2004). All these approaches use an extended future horizon to form the projection or regression from which an observable subspace is extracted. Yet there are other methods such as OKID of (Phan and Longman, 1992) and that of (Ljung and McKelvey, 1996) that do not use an extended horizon in the projection or regression step. Instead, a single high order ARX model is used.
A natural question is whether the future horizon is necessary and if so what role does it play in these steps. In this paper we investigate the role of the future horizon using the whitening »lter approach of (Chiuso and Picci, 2004), which works for both open-loop and closed-loop data. We conclude that the role of future horizon in this algorithm is merely extending the order of a bank of already high order ARX models. The di«erence from a single ARX model is insigni»cant if the ARX order or past horizon is su▒ciently high. The role of future horizon is mainly in the model reduction step where it serves to elevate the order of the Hankel matrix. We complement the analysis with simulations.