Abstract

A pressure servosystem, with compressed air as its primary power source, plays a pivotal role in automotive braking. The substitution of expensive proportional valves with high-speed switching valves (HSVs) for chamber pressure control remains a prominent challenge for researchers. In addressing the single-chamber dual-valve pressure tracking system, a novel approach is proposed using an adaptive neuro-fuzzy inference system (ANFIS) that enhances fuzzy control through neural network refinement. Integration with mode switching is employed to ameliorate chamber pressure tracking performance. This strategy amalgamates the learning capability of neural networks with the inferential capacity of fuzzy logic, effectively handling the intricate nonlinear characteristics of pneumatic systems. Experimental results demonstrate that for step signals in the range of 0.3–0.6 MPa, the maximum overshoot is reduced to 0.0041 MPa, and the random step error ranges between −0.01287 and 0.01275 MPa. The relative root-mean-square error for a 0.5 Hz harmonic signal is diminished by 26.91%.

References

1.
Rahmat
,
M. F.
,
Sunar
,
N. H.
,
Salim
,
S. N. S.
,
Zainal Abidin
,
M. S.
,
Mohd Fauzi
,
A. A.
, and
Ismail
,
Z. H.
,
2011
, “
Review on Modeling and Controller Design in Pneumatic Actuator Control System
,”
Int. J. Smart Sens. Intell. Syst.
,
4
(
4
), pp.
630
661
.10.21307/ijssis-2017-460
2.
Bruno
,
N.
,
Zhu
,
Y.
,
Liu
,
C.
,
Gao
,
Q.
, and
Li
,
Y.
,
2019
, “
Development of a Piezoelectric High Speed on/Off Valve and Its Application to Pneumatic Closed-Loop Position Control System
,”
J. Mech. Sci. Technol.
,
33
(
6
), pp.
2747
2759
.10.1007/s12206-019-0521-9
3.
Lin
,
Z.
,
Zhang
,
T.
,
Xie
,
Q.
, and
Wei
,
Q.
,
2018
, “
Intelligent Electro-Pneumatic Position Tracking System Using Improved Mode-Switching Sliding Control With Fuzzy Nonlinear Gain
,”
IEEE Access
,
6
, pp.
34462
34476
.10.1109/ACCESS.2018.2847637
4.
Goldstein
,
S. R.
, and
Richardson
,
H. H.
,
1968
, “
A Differential Pulse-Length Modulated Pneumatic Servo Utilizing Floating-Flapper-Disk Switching Valves
,”
ASME Basic Eng.
,
90
(
2
), pp.
143
151
.10.1115/1.3605072
5.
Fathi
,
M.
, and
Najafi
,
F.
,
2016
, “
Improved Tracking Accuracy of a Pneumatic Actuator on Entire Piston Stroke by a Modified fuzzy-PWM Controller
,”
J. Braz. Soc. Mech. Sci. Eng.
,
39
(
3
), pp. 879–893.10.1007/s40430-016-0663-y
6.
Nie
,
S.
,
Liu
,
X.
,
Yin
,
F.
,
Ji
,
H.
, and
Zhang
,
J.
,
2018
, “
Development of a High-Pressure Pneumatic On/Off Valve With High Transient Performances Direct-Driven by Voice Coil Motor
,”
Appl. Sci.
,
8
(
4
), p.
611
.10.3390/app8040611
7.
Zhang
,
X.
,
Lin
,
Z.
,
Li
,
J.
, and
Zhang
,
T.
,
2023
, “
Pneumatic Pressure Control Based on Improved NMPC and Its Application to Aeroengine Surge Simulation
,”
Chin. J. Aeronaut.
,
36
(
4
), pp.
468
485
.10.1016/j.cja.2022.12.019
8.
Zhu
,
Y.
, and
Jin
,
B.
,
2015
, “
Analysis and Modeling of a Proportional Directional Valve With Nonlinear Solenoid
,”
J. Braz. Soc. Mech. Sci. Eng.
,
38
(
2
), pp. 507–514.10.1007/s40430-015-0464-8
9.
Zhang
,
J.
,
Lv
,
C.
,
Yue
,
X.
,
Li
,
Y.
, and
Yuan
,
Y.
,
2014
, “
Study on a Linear Relationship Between Limited Pressure Difference and Coil Current of On/Off Valve and Its Influential Factors
,”
ISA Trans.
,
53
(
1
), pp.
150
161
.10.1016/j.isatra.2013.09.008
10.
Zhong
,
D.
,
Zhu
,
Y.-A.
,
Wang
,
L.
,
Duan
,
J.
, and
He
,
J.
,
2020
, “
Position Tracking of a Pneumatic-Muscle-Driven Rehabilitation Robot by a Single Neuron Tuned PID Controller
,”
Complexity
,
2020
, pp.
1
17
.10.1155/2020/8812459
11.
Du
,
H. W.
,
Yuan
,
T. H.
, and
Xiong
,
W.
,
2023
, “
Cylinder Position Control Driven by Pneumatic Digital Bridge Circuit Using a Fuzzy Algorithm Under Large Stroke and Varying Load Conditions
,”
J. Franklin Inst.
,
360
(
8
), pp.
5892
5909
.10.1016/j.jfranklin.2023.04.007
12.
Hejrati
,
B.
, and
Najafi
,
F.
,
2013
, “
Accurate Pressure Control of a Pneumatic Actuator With a Novel Pulse Width Modulation-Sliding Mode Controller Using a Fast Switching On/Off Valve
,”
Proc. Inst. Mech. Eng., Part I
,
227
(
2
), pp.
230
242
.
13.
Lin
,
Z.
,
Gan
,
J.
,
Qian
,
Q.
,
Huang
,
F.
,
Zhang
,
X.
,
Zhang
,
T.
, and
Liu
,
W.
,
2023
, “
Developing a Novel Gaussian Process Model Predictive Controller to Improve the Energy Efficiency and Tracking Accuracy of the Pressure Servo Control System
,”
J. Cleaner Prod.
,
417
, p.
138057
.10.1016/j.jclepro.2023.138057
14.
Leephakpreeda
,
T.
,
2011
, “
Fuzzy Logic Based PWM Control and Neural Controlled-Variable Estimation of Pneumatic Artificial Muscle Actuators
,”
Expert Syst. Appl.
,
38
(
6
), pp.
7837
7850
.10.1016/j.eswa.2010.12.120
15.
Mazare
,
M.
,
Taghizadeh
,
M.
, and
Kazemi
,
M. G.
,
2017
, “
Optimal Hybrid Scheme of Dynamic Neural Network and PID Controller Based on Harmony Search Algorithm to Control a PWM-Driven Pneumatic Actuator Position
,”
J. Vib. Control
,
24
(
16
), pp.
3538
3554
.10.1177/1077546317707102
16.
Yu
,
Q.
,
Wang
,
Q.
,
Tan
,
X.
, and
Li
,
X.
,
2021
, “
Water Spray Heat Transfer Gas Compression for Compressed Air Energy System
,”
Renewable Energy
,
179
, pp.
1106
1121
.10.1016/j.renene.2021.07.128
17.
Nguyen
,
T.
,
Leavitt
,
J.
,
Jabbari
,
F.
, and
Bobrow
,
J. E.
,
2007
, “
Accurate Sliding-Mode Control of Pneumatic Systems Using Low-Cost Solenoid Valves
,”
IEEE/ASME Trans. Mechatron.
,
12
(
2
), pp.
216
219
.10.1109/TMECH.2007.892821
18.
Mohd Adnan
,
M. R. H.
,
Sarkheyli
,
A.
,
Mohd Zain
,
A.
, and
Haron
,
H.
,
2015
, “
Fuzzy Logic for Modeling Machining Process: A Review
,”
Artif. Intell. Rev.
,
43
(
3
), pp.
345
379
.10.1007/s10462-012-9381-8
19.
Faustino
,
C. P.
,
Novaes
,
C. P.
,
Pinheiro
,
C. A. M.
, and
Carpinteiro
,
O. A.
,
2014
, “
Improving the Performance of Fuzzy Rules-Based Forecasters Through Application of FCM Algorithm
,”
Artif. Intell. Rev.
,
41
(
2
), pp.
287
300
.10.1007/s10462-011-9308-9
20.
Dewan
,
M. W.
,
Huggett
,
D. J.
,
Warren Liao
,
T.
,
Wahab
,
M. A.
, and
Okeil
,
A. M.
,
2016
, “
Prediction of Tensile Strength of Friction Stir Weld Joints With Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network
,”
Mater. Des.
,
92
, pp.
288
299
.10.1016/j.matdes.2015.12.005
21.
Erman
,
Ç.
, and
Karwowski
,
W.
,
2016
, “
Predicting the Occurrence of Adverse Events Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach With the Help of ANFIS Input Selection
,”
Artif. Intell. Rev.
,
48
(
2
), pp.
139
155
.10.1007/s10462-016-9497-3
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