Modeling and Identification of Nonlinear Systems: A Review of the Multimodel Approach—Part 2

Abstract

The efficacy of the multimodel framework (MMF) in modeling and identification of complex, nonlinear, and uncertain systems has been widely recognized in the literature owing to its simplicity, transparency, and mathematical tractability, allowing the use of well-known modeling analysis and control design techniques. The approach proved to be effective in addressing some of the shortcomings of other modeling techniques such as those based on a single nonlinear autoregressive network with exogenous inputs model or neural networks. A great number of researchers have contributed to this active field. Due to the significant amount of contributions and the lack of a recent survey, the review of recent developments in this field is vital. In this two-part paper, we attempt to provide a comprehensive coverage of the multimodel approach for modeling and identification of complex systems. This paper contains a classification of different methods, the challenges encountered, as well as recent applications of MMF in various fields. In this part 2, the review of multimodel internal structures and parameter estimation as well as validity computation methods is presented. In addition, a multimodel application and future direction are covered. In this literature survey, our main focus is on the MMF where the final system’s representation and behavior is generated through the interpolation of several possible local models. This is of prime importance to control designers. All through this paper, different active research areas and open problems are discussed.