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Sliding Mode Control on Receding Horizon : Practical Control Design and Application

Yin, Lianhao LU ; Turesson, Gabriel LU ; Tunestål, Per LU and Johansson, Rolf LU orcid (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.

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author
; ; and
organization
publishing date
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}},
}