An adaptive controller based on sliding mode condition is developed with estimated pseudopartial derivative (PPD) of data-driven scheme. The controlled plant is considered as a class of unknown discrete-time systems with only output feedback, which allows the proposed controller to be applicable for practical plants operated by computerization systems. The convergence of estimated PPD is analyzed by Lyapunov direct method under reasonable assumptions. The control law is derived by the estimated PPD and reaching condition of sliding surface as a model-free of controlled plant. The performance of the proposed control scheme is validated by theoretical analysis and experimental system with direct current (DC) motor current control.

References

References
1.
Hou
,
Z. S.
, and
Wang
,
Z.
,
2013
, “
From Model-Based Control to Data-Driven Control: Survey, Classification and Perspective
,”
Inf. Sci.
,
235
(20), pp.
3
35
.
2.
Zhang
,
C. L.
, and
Li
,
J. M.
,
2015
, “
Adaptive Iterative Learning Control of Non-Uniform Trajectory Tracking for Strict Feedback Nonlinear Time-Varying Systems With Unknown Control Direction
,”
Appl. Math. Modell.
,
39
(10–11), pp.
2942
2950
.
3.
Su
,
X.
,
Wu
,
L.
,
Shi
,
P.
, and
Song
,
Y.
,
2012
, “
H∞ Model Reduction of Takagi–Sugeno Fuzzy Stochastic Systems
,”
IEEE Trans. Syst., Man, Cybern., Part B
,
42
(
6
), pp.
1574
1585
.
4.
Wu
,
L.
,
Shi
,
P.
, and
Gao
,
H.
,
2010
, “
State Estimation and Sliding-Mode Control of Markovian Jump Singular Systems
,”
IEEE Trans. Autom. Control
,
55
(
5
), pp.
1213
1219
.
5.
Liu
,
Y. J.
, and
Tong
,
S.
,
2015
, “
Adaptive NN Tracking Control of Uncertain Nonlinear Discrete-Time Systems With Nonaffine Dead-Zone Input
,”
IEEE Trans. Cybern.
,
45
(
3
), pp.
497
505
.
6.
Hou
,
Z. S.
,
1994
, “
The Parameter Identification, Adaptive Control and Model Free Learning Adaptive Control for Nonlinear Systems
,” Ph.D. thesis, Northeastern University, Shenyang, China.
7.
Zhu
,
Y.
, and
Hou
,
Z. S.
,
2014
, “
Data-Driven MFAC for a Class of Discrete-Time Nonlinear Systems With RBFNN
,”
IEEE Trans. Neural Networks Learn. Syst.
,
25
(
5
), pp.
1013
2014
.
8.
Treesatayapun
,
C.
,
2015
, “
Data Input-Output Adaptive Controller Based on IF–THEN Rules for a Class of Non-Affine Discrete-Time Systems: The Robotic Plant
,”
J. Intell. Fuzzy Syst.
,
28
, pp.
661
668
.
9.
Treesatayapun
,
C.
,
2014
, “
Adaptive Control Based on IF–THEN Rules for Grasping Force Regulation With Unknown Contact Mechanism
,”
Rob. Comput. Integr. Manuf.
,
30
(
1
), pp.
11
18
.
10.
Chi
,
R.
,
Hou
,
Z.
, and
Jin
,
S.
,
2015
, “
A Data-Driven Adaptive ILC for a Class of Nonlinear Discrete-Time Systems With Random Initial States and Iteration-Varying Target Trajectory
,”
J. Franklin Inst.
,
352
(
6
), pp.
2407
2424
.
11.
Esmaeli
,
A.
,
2016
, “
Stability Analysis and Control of Microgrids by Sliding Mode Control
,”
Electr. Power Energy Syst.
,
78
(1), pp.
22
28
.
12.
Pai
,
M. C.
,
2014
, “
Global Synchronization of Uncertain Chaotic Systems Via Discrete-Time Sliding Mode Control
,”
Appl. Math. Comput.
,
227
(1), pp.
663
671
.
13.
Khandekar
,
A. A.
,
Malwatkar
,
G. M.
, and
Patre
,
B. M.
,
2013
, “
Discrete Sliding Mode Control for Robust Tracking of Higher Order Delay Time Systems With Experimental Application
,”
ISA Trans.
,
52
(
1
), pp.
36
44
.
14.
Castanos
,
F.
, and
Fridman
,
L.
,
2006
, “
Analysis and Design of Integral Sliding Manifolds for Systems With Unmatched Perturbations
,”
IEEE Trans. Autom. Control
,
51
(
5
), pp.
853
858
.
15.
Hwang
,
C. L.
, and
Chen
,
Y. M.
,
2004
, “
Discrete Sliding-Mode Tracking Control of High-Displacement Piezoelectric Actuator Systems
,”
ASME J. Dyn. Syst., Meas., Control
,
126
(
4
), pp.
721
731
.
16.
Boban
,
V.
,
Branislava
,
P. D.
, and
Cedomir
,
M.
,
2010
, “
Improved Discrete-Time Sliding-Mode Position Control Using Euler Velocity Estimation
,”
IEEE Trans. Ind. Electron.
,
57
, pp.
3840
3847
.
17.
Atia
,
M. R. A.
,
Haggag
,
S. A.
, and
Kamal
,
A. M. M.
,
2016
, “
Enhanced Electromechanical Brake-by-Wire System Using Sliding Mode Controller
,”
ASME J. Dyn. Syst., Meas., Control
,
138
(
4
), p.
041003
.
18.
Bai
,
R.
,
2015
, “
Neural Network Control-Based Adaptive Design for a Class of DC Motor Systems With the Full State Constraints
,”
Neurocomputing
,
168
(1), pp.
65
69
.
19.
Rubaai
,
A.
,
Castro-Sitiriche
,
M. J.
, and
Ofoli
,
A. R.
,
2008
, “
Design and Implementation of Parallel Fuzzy PID Controller for High-Performance Brushless Motor Drives: An Integrated Environment for Rapid Control Prototyping
,”
IEEE Trans. Ind. Appl.
,
44
(
4
), pp.
1090
1098
.
20.
Dhanya
,
K.
,
Panicker
,
M.
, and
Mol
,
R.
,
2013
, “
Hybrid PI-Fuzzy Controller for Brushless DC Motor Speed Control
,”
IOSR J. Electr. Electron. Eng.
,
8
(
6
), pp.
33
43
.
21.
Bharatkar
,
S. S.
,
Yanamshetti
,
R.
,
Chatterjee
,
D.
, and
Ganguli
,
A. K.
,
2011
, “
Dual-Mode Switching Technique for Reduction of Commutation Torque Ripple of Brushless DC Motor
,”
IET Electr. Power Appl.
,
5
(
1
), pp.
193
202
.
22.
Fakham
,
H.
,
Djemai
,
M.
, and
Busawon
,
K.
,
2008
, “
Design and Practical Implementation of Aback-EMF Sliding-Mode Observer for a Brushless DC Motor
,”
IET Electr. Power Appl.
,
2
(
6
), pp.
353
361
.
23.
Mozaffari-Niapour
,
S.
,
Tabarraie
,
M.
, and
Feyzi
,
M.
,
2012
, “
Design and Analysis of Speed-Sensorless Robust Stochastic L∞-Induced Observer for High-Performance Brushless DC Motor Drives With Diminished Torque Ripple
,”
Energy Convers. Manage.
,
64
(1), pp.
482
498
.
You do not currently have access to this content.