Identification of Multiple‐Mode Linear Models Based on Particle Swarm Optimizer with Cyclic Network Mechanism

This paper studies the metaheuristic optimizer‐based direct identification of a multiple‐mode system consisting of a finite set of linear regression representations of subsystems. To this end, the concept of a multiple‐mode linear regression model is first introduced, and its identification issues are established. A method for reducing the identification problem for multiple‐mode models to an optimization problem is also described in detail. Then, to overcome the difficulties that arise because the formulated optimization problem is inherently ill‐conditioned and nonconvex, the cyclic‐network‐topology‐based constrained particle swarm optimizer (CNT‐CPSO) is introduced, and a concrete procedure for the CNT‐CPSO‐based identification methodology is developed. This scheme requires no prior knowledge of the mode transitions between subsystems and, unlike some conventional methods, can handle a large amount of data without difficulty during the identification process. This is one of the distinguishing features of the proposed method. The paper also considers an extension of the CNT‐CPSO‐based identification scheme that makes it possible to simultaneously obtain both the optimal parameters of the multiple submodels and a certain decision parameter involved in the mode transition criteria. Finally, an experimental setup using a DC motor system is established to demonstrate the practical usability of the proposed metaheuristic optimizer‐based identification scheme for developing a multiple‐mode linear regression model.