Multi-model predictive control of Hammerstein-Wiener systems based on balanced multi-model partition

Jingjing Du, Lei Zhang, Junfeng Chen, Jian Li and Changping Zhu

Mathematical and Computer Modelling of Dynamical Systems, 2019, vol. 25, issue 4, 333-353

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.

Date: 2019
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DOI: 10.1080/13873954.2019.1624580

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