Self-adjusted decomposition for multi-model predictive control of Hammerstein systems based on included angle

• Published in: ISA transactions Vol. 103; pp. 19 - 27
• Main Authors: Du, Jingjing, Zhang, Lei, Chen, Junfeng, Li, Jian, Jiang, XuePing, Zhu, Changping
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2020
• Subjects: Balanced decomposition; Hammerstein systems; Included angle; Multi-model predictive control; Self-adjusted decomposition; Self-adjusted decomposition; Balanced decomposition; Multi-model predictive control; Included angle; Hammerstein systems
• ISSN: 0019-0578; 1879-2022
• DOI: 10.1016/j.isatra.2020.03.028

A self-adjusted multi-model decomposition (SAMMD) method is proposed based on included angle (IA) to realize multi-model predictive control (MMPC) of Hammerstein systems. Given an initial value for threshold and a step-size, a balanced decomposition in terms of Measurement of Nonlinearity (MoN) is obtained with an appropriate linear model set to approximate the considered Hammerstein system. Based on the linear model set, a MMPC is designed for set-point tracking and anti-disturbance control using an offline weighting method. Thus, time-consuming tuning of threshold value is largely avoided; reliance on experience is greatly decreased. And the efficiency and quality of decomposition are largely raised. A CSTR process and a Lab-tank system that can be approximated by Hammerstein models are investigated. Simulations illustrate that the proposed SAMMD method is both effective and efficient. •A self-adjusted decomposition method is proposed based on included angle.•The proposed method is used for multi-model predictive control of Hammerstein systems.•A balanced decomposition can be obtained easily using the proposed method.•Complex parameter tuning of threshold value is avoided in the proposed method.•Efficiency and quality of multi-model decomposition is raised.