A novel weighting method for multi-linear MPC control of Hammerstein systems based on included angle

Jingjing Du; Lei Zhang; QingBang Han

doi:10.1016/j.isatra.2018.06.008

A novel weighting method for multi-linear MPC control of Hammerstein systems based on included angle

ISA Trans. 2018 Sep:80:212-220. doi: 10.1016/j.isatra.2018.06.008. Epub 2018 Jun 19.

Authors

Jingjing Du¹, Lei Zhang², QingBang Han³

Affiliations

¹ College of Internet of Things Engineering, Hohai University, Changzhou 213022, China. Electronic address: hzdujing@163.com.
² College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
³ College of Internet of Things Engineering, Hohai University, Changzhou 213022, China.

PMID: 29929877
DOI: 10.1016/j.isatra.2018.06.008

Abstract

A novel included angle based weighting method is proposed for multi-linear model predictive control (MPC) of Hammerstein systems. It makes full use of the special structure of the Hammerstein models, and thus it is intuitive and simple. Moreover, there is only one tuning parameter and the weights can be calculated offline and stored in a look-up table. Therefore, online computational load is largely reduced. Most important of all, it schedules local controllers properly and effectively. A Lab-tank system which can be modeled into a Hammerstein model is investigated. Comparisons are made among the nonlinearity inversion control method, the proposed weighting method and traditional weighting methods, e.g., Trapezoidal and Gaussian weighting methods. Simulations confirm that the proposed weighting method is superior to traditional methods.

Keywords: Hammerstein system; Included angle; Multi-linear MPC; Tuning parameter; Weighting.