A new information-weighted recursive algorithm for time-varying systems: application to UAV system identification. Issue 11 (18th August 2018)

• Record Type: Journal Article
• Authors: Liu, Zun; Ji, Honghai; Pei, Hailong; Lewis, Frank L.
• Is Part Of: International journal of systems science. Volume 49:Issue 11(2018)
• Page Start: 2477
• Page End: 2489
• Publication Date: 2018-08-18
• Subjects: System identification -- experience replay -- unmanned aerial vehicle -- online-identification method -- time-varying system; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2018.1505004
• Languages: English
• ISSNs: 0020-7721

Abstract

ABSTRACT: This paper presents a new recursive identification method which can efficiently estimate time-varying parameters in discrete time systems and has significant advantages over standard recursive least-squares (RLS) method. This new information-weighted recursive algorithm for time-varying systems has three novel features, discounting of inaccurate estimates through weighting by the Information matrix, using the reuse of past data in computing current parameter estimates, a new tuneable damping factor parameter and a precisely designed compensation term to neutralise the estimation error caused by time-varying coefficients. A rigorous proof of convergence is also provided. Simulations show that the new algorithm significantly outperforms standard RLS, exhibiting better tracking performance and faster convergence. Flight tests on a T-REX 800 helicopter Unmanned Aerial Vehicle platform show that it gives system parameter estimates that are accurate enough and converge quickly enough that flight controllers can be designed in real-time based on the online identified model.