Parameter identification based on prescribed estimation error performance for extended Wiener–Hammerstein systems. Issue 2 (1st January 2020)

• Record Type: Journal Article
• Authors: Li, Linwei; Ren, Xuemei
• Is Part Of: IET control theory & applications. Volume 14:Issue 2(2020)
• Page Start: 304
• Page End: 312
• Publication Date: 2020-01-01
• Subjects: nonlinear control systems -- parameter estimation -- identification -- convergence -- adaptive control; accurate parameter estimation -- available identification approaches -- parameter identification -- prescribed estimation error performance -- extended Wiener–Hammerstein systems -- identification algorithm -- prescribed error -- parameter estimation error information -- poor transient performance -- identification process -- novel identification scheme -- prescribed performance function -- error transformation technique -- prescribed estimation error problem -- equivalent generalised error problem -- parameter updating law; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2018.5217
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : Most identification algorithms do not consider the prescribed error bound on the parameter estimation error information, which may result in the poor transient performance of parameter estimation during the identification process. In this study, a novel identification scheme is presented for the extended Wiener–Hammerstein systems with backlash non‐linearity, which is implemented by using prescribed performance function and error transformation technique. To improve the transient performance of parameter estimation, the prescribed performance function is designed to prescribe estimation error bound. Then, the error transformation technique is applied to transform the prescribed estimation error problem into an equivalent generalised error problem, which can simplify the design of identification algorithm. By developing the parameter updating law, the convergence of generalised error problem can be guaranteed, and the parameter estimation of considered system can be achieved. The numerical example and experiment results validate that the proposed scheme can provide more accurate parameter estimation and better transient performance than the available identification approaches.