Abstract
It poses significant challenge to control Hammerstein–Wiener systems involving modeling nonlinearities. In this paper, a novel data-driven predictive control method based on the subspace identification of Hammerstein–Wiener systems is presented. By reformulating the open- and closed-loop Hammerstein–Wiener model, subspace predictions of the outputs are derived using recursive substitution of the Hankel matrices. The output nonlinearity is presented by polynomial representation and the subspace predictors are obtained using the QR decomposition, together with additional algebra manipulations, where Q is an orthogonal matrix and R is an upper triangular matrix. The predictors are applied to the model predictive controller, wherein the integrated action is successfully incorporated. The effectiveness and feasibility of the proposed controller is also verified by numerical simulation on a fermentation bioreactor system.
| Original language | English |
|---|---|
| Pages (from-to) | 447-461 |
| Number of pages | 15 |
| Journal | Information Sciences |
| Volume | 422 |
| Early online date | 5 Sept 2017 |
| DOIs | |
| Publication status | Published - Jan 2018 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2017 Elsevier Inc.
Funding
This work is supported in part by the Major State Basic Research Development Program 973 (no. 2012CB215202), the National Natural Science Foundation of China (no. 61773081 , 61134001), Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Technology Transformation Program of Chongqing Higher Education University (no. KJZH17102) and Scientific and Technological Research Program of Chongqing Municipal Education Commission (no. KJ1503008).
Keywords
- Data-driven predictive control
- Hammerstein–Wiener systems
- Subspace identification
- The fermentation bioreactor system