Abstract
The complicated physical and chemical reactions in the internal complex operating environment of smelting process and the Blast Furnace (BF) have led to the difficulty of establishing the model-based controllers. Therefore, model free control methods should be used that meet the actual needs of the engineering systems. However, due to the sparse characteristic of the molten iron quality (MIQ) data in BF ironmaking, traditional model free adaptive control based MIQ control methods cannot control such a complex industrial system with strong nonlinear time-varying dynamics. In this paper, an extended and compact form dynamic linearization (CFDL) based model free adaptive predictive control (MFAPC) scheme (CFDL-MFAPC) is proposed for multivariate MIQ indices by generalizing the CFDL-MFAPC method only for SISO system to MIMO system. Two groups of verification experiments are performed to evaluate the performance of the controller. The results show that the proposed method has not only a better control performance than the compared traditional CFDL based model free adaptive control method and data-driven model predictive control (MPC) method, but also can guarantee the bounded-input bounded-output stability of the MIQ output of the control system for BF ironmaking process.
| Original language | English |
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| Title of host publication | 2018 IEEE Conference on Decision and Control, CDC 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2617-2622 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538613955 |
| DOIs | |
| State | Published - Jul 2 2018 |
| Externally published | Yes |
| Event | 57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States Duration: Dec 17 2018 → Dec 19 2018 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Volume | 2018-December |
| ISSN (Print) | 0743-1546 |
| ISSN (Electronic) | 2576-2370 |
Conference
| Conference | 57th IEEE Conference on Decision and Control, CDC 2018 |
|---|---|
| Country/Territory | United States |
| City | Miami |
| Period | 12/17/18 → 12/19/18 |
Funding
*Research supported in part by the National Science Foundation of China under Grant 61333007, Grant 61473064, Grant 61290323, and Grant 61790572, in part by the Research Funds for the Central Universities under Grant N130108001, in part by the 111 Project under Grant B08015, in part by the Project on Scientific Research for the EDLN under Grant L20150186, and in part by the State (Beijing) Key Laboratory of Process Automation in Mining & Metallurgy (BGRIMM-KZSKL-2017-04).