Abstract
One of the challenging control problems of an underground coal gasification (UCG) process involves maintaining a desired heating value from the extracted product gases. In this paper, a model-based control and state estimation of UCG process is described. For the purpose of control and state estimation, a sophisticated model of the UCG process using partial differential equations is approximated by a gain-scheduled nonlinear control-oriented model. Based on this approximated plant model, a robust integral sliding mode control is designed to track a desired trajectory of the heating value. Furthermore, for the estimation of unknown states of the system, a gain-scheduled modified Utkin observer is designed as well. The robustness of the nonlinear control and estimation techniques is assessed by introducing parametric uncertainties in the UCG plant. The simulation results highlight the effectiveness of the proposed nonlinear control and estimation techniques in comparison to a conventional PI controller.
| Original language | English |
|---|---|
| Title of host publication | 2018 23rd International Conference on Methods and Models in Automation and Robotics, MMAR 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 357-362 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538643259 |
| DOIs | |
| State | Published - 8 Oct 2018 |
| Externally published | Yes |
| Event | 23rd International Conference on Methods and Models in Automation and Robotics, MMAR 2018 - Miedzyzdroje, Poland Duration: 27 Aug 2018 → 30 Aug 2018 |
Publication series
| Name | 2018 23rd International Conference on Methods and Models in Automation and Robotics, MMAR 2018 |
|---|
Conference
| Conference | 23rd International Conference on Methods and Models in Automation and Robotics, MMAR 2018 |
|---|---|
| Country/Territory | Poland |
| City | Miedzyzdroje |
| Period | 27/08/18 → 30/08/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
ASJC Scopus subject areas
- Artificial Intelligence
- Mechanical Engineering
- Control and Optimization