Robust adaptive identification for sandwich systems with unknown time-delay. (May 2020)

• Record Type: Journal Article
• Authors: Li, Linwei; Zhang, Huanlong; Ren, Xuemei
• Is Part Of: ISA transactions. Volume 100(2020)
• Page Start: 289
• Page End: 298
• Publication Date: 2020-05
• Subjects: Sandwich systems -- Adaptive parameter estimation -- Filtering technique -- Cost function -- Time-delay; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.12.005
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: A discrete time, robust adaptive estimator is developed to identify the time-delay and sandwich systems parameters. To obtain explicitly the expression of delay parameter, the observation and augmented data are reconstructed. By using the filter operator and some auxiliary vectors, the parameter identification error vector is derived. Based on the parameter identification error term and initial estimation error term, a novel criterion function is invented. In comparison to the common criterion function, a classy estimation property is provided based on the criterion function in that paper because the identification error term can lift estimation accuracy, and the initial estimation error term speeds up the convergence. Under the persistent excitation condition, convergence of the developed estimator is analyzed. The availability and superiority of proposed identification scheme are verified by both numerical simulation and a turntable servo system. Highlights: To extract the delay parameters from the system, the augmented parameter vector and observation vector are reconstructed. All the parameters can be estimated. Compared with the existing filtering approaches [45–47], the assumptions for filter are relaxed and only one filter operator needs to be designed, which facilitate filter structure. Compared to the prediction error method [28, 31, 33, 40], the loss function of this paper contains the discount of identification error and the penalty of initial error. The discount term lifts the estimation precision. The penalty term produces faster convergence. In comparison as our previous work [49, 52], the influence of filtered noise on the identification performance is considered and the gain matrix Γ ( t ) is a recursive form of this paper.