Auxiliary model-based multi-innovation PSO identification for Wiener–Hammerstein systems with scarce measurements. (November 2021)

• Record Type: Journal Article
• Authors: Zong, Tiancheng; Li, Junhong; Lu, Guoping
• Is Part Of: Engineering applications of artificial intelligence. Volume 106(2021)
• Publication Date: 2021-11
• Subjects: Wiener–Hammerstein systems -- Scarce measurements -- Multi-innovation -- Auxiliary model -- Particle swarm optimization -- System identification; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104470
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In many actual systems, it is often difficult to obtain complete input and output data. Thus, the problem of scarce measurements usually appears in the identification of these systems. This article investigates the parameter estimation of Wiener–Hammerstein systems with scarce measurements. A Wiener–Hammerstein system comprises an input linear unit, a nonlinear unit, and an output linear unit. The nonlinear unit in this paper is described by the saturation and dead-zone characteristics respectively. To solve the incomplete data problem caused by scarce measurements, the auxiliary model is applied. Then the auxiliary model-based improved particle swarm optimization (PSO) algorithm is derived. Furthermore, the multi-innovation technology is introduced to improve the convergence speed and estimation accuracy, and the auxiliary model-based multi-innovation improved PSO is proposed. Finally, the simulations of two numerical examples and the application of the turntable servo system indicate that the proposed multi-innovation method is applicable to Wiener–Hammerstein models with scarce measurements, the estimation accuracy and convergence speed are greatly improved. Highlights: The Wiener–Hammerstein systems with scarce measurements are identified. The auxiliary model-based improved PSO algorithm is derived. The multi-innovation is introduced to improve the convergence rate and accuracy. The nonlinear unit is described by the saturation and dead-zone characteristics.