TY  - INPR
TI  - Data-driven integral sliding mode predictive control with optimal disturbance observer
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10197791
UR  - http://dx.doi.org/10.1016/j.jfranklin.2024.107278
Y1  - 2024/09//
PB  - Elsevier BV
JF  - Journal of the Franklin Institute
SN  - 0016-0032
A1  - Xia, Rui
A1  - Song, Xiaohang
A1  - Zhang, Dawei
A1  - Zhao, Dongya
A1  - Spurgeon, Sarah K
N2  - In this paper, a novel data-driven integral sliding mode predictive control algorithm based on an optimal disturbance observer
(DDISMPC-ODO) is proposed for a class of nonlinear discrete-time systems (NDTS) subject to external disturbances. The designed optimal disturbance observer realizes the precise observation of the lumped disturbance, thus ameliorating the accuracy of
the controller and weakening problems with chattering. In this work, a robust pseudo-partial derivative (PPD) estimation algorithm
is introduced, which not only improves the system performance, but also facilitates theoretical proof of parameter estimation and
tracking accuracy. The convergence of the PPD estimation error and disturbance observation error is proved. It is also proved
that the accuracy of the disturbance observation error can converge to O(T
3
) and then the magnitude of the sliding variable and
the tracking error are also reduced to O(T
3
) respectively. Finally, the effectiveness of the proposed method is demonstrated by a
simulation example and an experiment.
KW  - Nonlinear discrete-time systems
KW  -  model-free adaptive control
KW  -  optimal disturbance observer
KW  -  robust PPD estimator
KW  -  tracking
accuracy
AV  - restricted
ER  -