Sliding Mode Control on Receding Horizon : Practical Control Design and Application
(2021) In Control Engineering Practice 109.- Abstract
Sliding mode control (SMC) is to keep the system to a stable differential manifold. Model predictive control (MPC) calculates the control input by solving an optimization problem on receding horizon. The method of receding horizon sliding control (RHSC) includes the predicted information into the SMC design by combining SMC and MPC. Considering the modeling error and measurement noise, there are model-mismatch and disturbance problems in control practice. This paper combines the demonstrated method of RHSC with a state-augmented Kalman filter addressing the model mismatch and disturbance problem. The proposed scheme has been applied to the air system of an advanced heavy-duty engine. The results have shown the capability of tracking the... (More)
Sliding mode control (SMC) is to keep the system to a stable differential manifold. Model predictive control (MPC) calculates the control input by solving an optimization problem on receding horizon. The method of receding horizon sliding control (RHSC) includes the predicted information into the SMC design by combining SMC and MPC. Considering the modeling error and measurement noise, there are model-mismatch and disturbance problems in control practice. This paper combines the demonstrated method of RHSC with a state-augmented Kalman filter addressing the model mismatch and disturbance problem. The proposed scheme has been applied to the air system of an advanced heavy-duty engine. The results have shown the capability of tracking the reference signal during a step-response test and the convergence rate to the target signal is 10% faster than MPC.
(Less)
- author
- Yin, Lianhao LU ; Turesson, Gabriel LU ; Tunestål, Per LU and Johansson, Rolf LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Kalman filter, Linear system, Model predictive control (MPC), Optimization, Receding horizon sliding control (RHSC), Sliding mode control (SMC)
- in
- Control Engineering Practice
- volume
- 109
- article number
- 104724
- publisher
- Elsevier
- external identifiers
-
- scopus:85099515891
- ISSN
- 0967-0661
- DOI
- 10.1016/j.conengprac.2021.104724
- project
- KCFP, Closed-Loop Combustion Control
- language
- English
- LU publication?
- yes
- id
- bf67bd5a-3d21-4038-926e-0a247a281d3c
- date added to LUP
- 2021-01-28 09:52:06
- date last changed
- 2022-08-10 10:56:54
@article{bf67bd5a-3d21-4038-926e-0a247a281d3c, abstract = {{<p>Sliding mode control (SMC) is to keep the system to a stable differential manifold. Model predictive control (MPC) calculates the control input by solving an optimization problem on receding horizon. The method of receding horizon sliding control (RHSC) includes the predicted information into the SMC design by combining SMC and MPC. Considering the modeling error and measurement noise, there are model-mismatch and disturbance problems in control practice. This paper combines the demonstrated method of RHSC with a state-augmented Kalman filter addressing the model mismatch and disturbance problem. The proposed scheme has been applied to the air system of an advanced heavy-duty engine. The results have shown the capability of tracking the reference signal during a step-response test and the convergence rate to the target signal is 10% faster than MPC.</p>}}, author = {{Yin, Lianhao and Turesson, Gabriel and Tunestål, Per and Johansson, Rolf}}, issn = {{0967-0661}}, keywords = {{Kalman filter; Linear system; Model predictive control (MPC); Optimization; Receding horizon sliding control (RHSC); Sliding mode control (SMC)}}, language = {{eng}}, publisher = {{Elsevier}}, series = {{Control Engineering Practice}}, title = {{Sliding Mode Control on Receding Horizon : Practical Control Design and Application}}, url = {{http://dx.doi.org/10.1016/j.conengprac.2021.104724}}, doi = {{10.1016/j.conengprac.2021.104724}}, volume = {{109}}, year = {{2021}}, }