Enhanced variable universe fuzzy proportional–integral–derivative control of structural vibration with real-time adaptive contracting–expanding factors
Restricted accessResearch articleFirst published online August, 2022
Enhanced variable universe fuzzy proportional–integral–derivative control of structural vibration with real-time adaptive contracting–expanding factors
A conventional variable universe fuzzy proportional–integral–derivative control approach is widely used for semi-active control in mechanical engineering. The performance of the controller is dependent on an optimal selection of parameters of the contracting–expanding factors. An improved variable universe fuzzy proportional–integral–derivative control algorithm is developed in this study where these parameters are automatically determined in real-time according to the error in the controlled responses and its change rate based on fuzzy logic control. The proposed method is numerically and experimentally illustrated with a three-story frame structure with a magnetorheological damper. The amplitude of displacement, velocity, and acceleration at all floor levels under the proposed control method are smaller than those obtained from existing proportional–integral–derivative, fuzzy, and conventional variable universe fuzzy methods.
BatanovicVPetrovicDPetrovicR (2009) Fuzzy logic based algorithms for maximum covering location problems. Information Sciences179(1–2): 120–129.
2.
CaoZZhengS (2017) MR-SAS and electric power steering variable universe fuzzy PID integrated control. Neural Computing and Applications31(4): 1249–1258.
3.
ChrysostomouCZDemetriouTPittasM, et al. (2005) Retrofit of a church with linear viscous dampers. Structural Control and Health Monitoring12(2): 197–212.
4.
CuiBXYangJPFangB (2007) New type of adaptive fuzzy controllers based on variable universe in application of traffic signal control. Journal of System Simulation19(2): 380–383.
5.
DebnathMKJenaTSanyalSK (2019) Frequency control analysis with PID fuzzy PID hybrid controller tuned by modified GWO technique. International Transactions on Electrical Energy Systems29(10): 1–21.
6.
GongXFLiHXDengGN (2009) Variable universe adaptive fuzzy control on the triple inverted pendulum and choosing contraction-expansion factor. In: International conference on artificial intelligence and computational intelligence, Shanghai, China, 7–8 November 2009, 63–67. California: IEEE Computer Soc.
7.
HuWGaoYLYangBT (2019) Semi-active vibration control of two flexible plates using an innovative joint mechanism. Mechanical Systems and Signal Processing130: 565–584.
8.
ImaduddinFMazlanSAZamzuriH (2013) A design and modelling review of rotary magnetorheological damper. Materials & Design51: 575–591.
9.
JangJ-SRSunC-T (1995) Neuro-fuzzy modeling and control. Proceedings of the IEEE83(3): 378–406.
10.
KasemiBMuthalifAGA.RashidMM, et al. (2012) Fuzzy-PID controller for semi-active vibration control using magnetorheological fluid damper. Procedia Engineering41: 1221–1227.
11.
KumarGKumarAJakkaRS (2018) The particle swarm modified quasi bang-bang controller for seismic vibration control. Ocean Engineering166: 105–116.
12.
KuoWHWuTNGuoJW, et al. (2006) Design and performance evaluation of a serial multi-electrode electrorheological damper. Journal of Sound and Vibration292(3–5): 694–709.
13.
KurataNKoboriTTakahashiM, et al. (1999) Actual seismic response controlled building with semi-active damper system. Earthquake Engineering & Structural Dynamics28(11): 1427–1447.
14.
LiH (1999) Adaptive fuzzy controllers based on variable universe. Science in China Series E: Technological Sciences42(1): 10–20.
15.
LiHMiaoZHWangJY (2002) Variable universe adaptive fuzzy control on the quadruple inverted pendulum. Science in China Series E45(2): 213–224.
16.
LinP-YLinT-K (2012) Control of seismically isolated bridges by magnetorheological dampers and a rolling pendulum system. Structural Control and Health Monitoring19(2): 278–294.
17.
LiuJLampinenJ (2002) A fuzzy adaptive differential evolution algorithm. In: IEEE Region 10 conference on computers, communications, control and power engineering, Beijing, China, 28–31 October 2002, pp. 606–611. New York: IEEE.
18.
LiuXYLiLFChenWF (2007) A variable universe fuzzy control algoeith based on fuzzy inference. International Conference on Wavelet Analysis and Pattern Recognition7: 453–457.
19.
LeeCC (1990) Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Transactions on Systems, Man, and Cybernetics20(2): 404–418.
20.
NingDDuHSunS, et al. (2019) A novel electrical variable stiffness device for vehicle seat suspension control with mismatched disturbance compensation. IEEE/ASME Transactions on Mechatronics24(5): 2019–2030.
21.
OhSYParkDJ (1995) Self-tuning fuzzy controller with variable universe of discourse. In: Proceedings of the IEEE international conference on systems, man and cybernetics, Washington, United States, 20–24 March 1995, 2628–2632. Vancouver, Canada: IEEE.
22.
QuCHuoLLiH, et al. (2014) A double homotopy approach for decentralized H∞ control of civil structures. Structural Control and Health Monitoring21(3): 269–281.
23.
PangHLiuFLiuX (2016) Enhanced variable-universe fuzzy control for vehicle semi-active suspension systems. Journal of Intelligent & Fuzzy Systems31(6): 2999–3006.
24.
PangHLiuFXuZ (2018) Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization. Neurocomputing306: 130–140.
25.
ParkK-SOkS-Y (2015) Modal-space reference-model-tracking fuzzy control of earthquake excited structures. Journal of Sound and Vibration334: 136–150.
26.
PengYBGhanemRLiJ (2013) Generalized optimal control policy for stochastic optimal control of structures. Structural Control and Health Monitoring20(2): 67–89.
27.
ShiPWuBSpencerBF, et al. (2016) Real-time hybrid testing with equivalent force control method incorporating Kalman filter. Structural Control and Health Monitoring23(4): 735–748.
28.
SongGSethiVLiH-N (2006) Vibration control of civil structures using piezoceramic smart materials: A review. Engineering Structures28(11): 1513–1524.
29.
SpencerBFDykeSJSainMK, et al.1997) Phenomenological model for magnetorheological dampers. Journal of Engineering Mechanics123(3): 230–238.
30.
WuBZhouH (2014) Sliding mode for equivalent force control in real-time substructure testing. Structural Control and Health Monitoring21(10): 1284–1303.
31.
XuYBLongZQLiuC (2012) Fuzzy control algorithm based on variable universe of discourse and its expansion-contraction factors. Journal of Convergence Information Technology7(19): 570–577.
32.
YangGSpencerBFCarlsonJD, et al. (2002) Large-scale MR fluid dampers: modeling and dynamic performance considerations. Engineering Structures24(3): 309–323.
33.
ZhangYWangJLiH (2012) Stabilization of the quadruple inverted pendulum by variable universe adaptive fuzzy controller based on variable gain H ∞ regulator. Journal of Systems Science and Complexity25(5): 856–872.
34.
ZhangLYCaoADuYX (2016) Variable universe fuzzy control for excitation system of HTS machine. Journal of Intelligent and Fuzzy Systems29(6): 2457–2465.
35.
ZhaoYTWangJXieXL, et al. (2009) Variable universe fuzzy control based on multilayer ant colony algorithm. Pattern Recognition and Artificial Intelligence22(5): 794–798.
36.
ZhengJChenJOuyangQ (2018) Variable universe fuzzy control for battery equalization. Journal of Systems Science and Complexity31(1): 325–342.
37.
ZhuXBChenKSWangYN, et al. (2019) Adaptive PID controller for cloud smart city system stability control based on chaotic neural network. Cluster Computing-the Journal of Networks Software Tools and Applications22(6): 13067–13075.
