Multi-model predictive control of Hammerstein-Wiener systems based on balanced multi-model partition. Issue 4 (4th July 2019)

• Record Type: Journal Article
• Authors: Du, Jingjing; Zhang, Lei; Chen, Junfeng; Li, Jian; Zhu, Changping
• Is Part Of: Mathematical and computer modelling of dynamical systems. Volume 25:Issue 4(2019)
• Page Start: 333
• Page End: 353
• Publication Date: 2019-07-04
• Subjects: Dichotomy gridding -- balanced multi-model partition -- Hammerstein-Wiener model -- included angle -- multi-MPC control; Engineering -- Mathematical models -- Periodicals; Computer simulation -- Periodicals; 515.39
• DOI: 10.1080/13873954.2019.1624580
• Languages: English
• ISSNs: 1387-3954

Abstract

ABSTRACT: Model analysis of Hammerstein-Wiener systems has been made, and it is found that the included angle is applicable to such systems to measure the non-linearity. Then, a dichotomy gridding algorithm is proposed based on the included angle. Supporting by the gridding algorithm, a balanced multi-model partition method is put forward to partition a Hammerstein-Wiener system into a set of local linear models. For each linear model, a linear model predictive controller (MPC) is designed. After that, a multi-MPC is composed of the linear MPCs via soft switching. Thus, a complex non-linear control problem is transformed into a set of linear control problems, which simplifies the original control problem and improves the control performance. Two non-linear systems are built into Hammerstein-Wiener models and investigated using the proposed methods. Simulations demonstrate that the proposed gridding and partition methods are effective, and the resulted multi-MPC controller has satisfactory performance in both set-point tracking and disturbance rejection control.