This study experimentally verifies robustness of a model-free vibration controller based on a virtual controlled object (VCO) considering parametric uncertainty of actuator. A proof-mass actuator, which can be modeled as a single-degree-of-freedom (SDOF) system, is used. A VCO, which is defined as an SDOF structure, is introduced between a real controlled object and the actuator model. The parameters of the VCO are determined so as to achieve model-free vibration control. A state equation to derive the model-free controller is constructed using the two-degree-of-freedom (2DOF) structure composed of the actuator model and the VCO. The parametric uncertainty of the actuator is quantitatively characterized in the 2DOF structure. The mixed control theory is used to design a model-free controller. The vibration suppression performance and robustness to the actuator uncertainty of the proposed method are validated by experiments. Simulation studies are also conducted to enhance the validity of the experimental results. As a result, the proposed damping method exhibits good damping performance and strong robustness to the actuator uncertainty and characteristic changes in controlled object.
WangLLiJYangY, et al. Active control of low-frequency vibrations in ultra-precision machining with blended infinite and zero stiffness. Int J Mach Tools Manuf2019; 139: 64–74.
2.
WangWLiYShiJ, et al. Vibration control method for an electric city bus driven by a dual motor coaxial series drive system based on model predictive control. IEEE Access2018; 6: 41188–41200.
3.
TangJCaoDYuT.Decentralized vibration control of a voice coil motor-based Stewart parallel mechanism: Simulation and experiments. Proc IMechE, Part C: J Mechanical Engineering Science2019; 233: 132–145.
4.
YonezawaHKajiwaraISatoS, et al. Vibration control of automotive drive system with nonlinear gear backlash. J Dyn Syst Meas Control Trans ASME2019; 141: 121002.
5.
YonezawaHKajiwaraINishidomeC, et al. Active vibration control of automobile drivetrain with backlash considering time-varying long control period. Proc IMechE, Part D: J Automobile Engineering2021; 235: 773–783.
6.
YonezawaHKajiwaraINishidomeC, et al. Vibration control of automotive drive system with backlash considering control period constraint. J Adv Mech Des Syst Manuf2019; 13: 1–16.
7.
KantMParameswaranAP.Modeling of low frequency dynamics of a smart system and its state feedback based active control. Mech Syst Signal Process2018; 99: 774–789.
8.
HaoSYamashitaYKobayashiK.Active vibration control of nonlinear 2DOF mechanical systems via IDA-PBC. IEICE Trans Fundam Electron Commun Comput Sci2020; E103A: 1078–1085.
9.
MeurersTVeresSMTanACH. Model-free frequency domain iterative active sound and vibration control. Control Eng Pract2003; 11: 1049–1059.
10.
WeiCLuoJDaiH, et al. Adaptive model-free constrained control of postcapture flexible spacecraft: a Euler–Lagrange approach. J Vib Control2018; 24: 4885–4903.
11.
YousefiHHirvonenMHandroosH, et al. Application of neural network in suppressing mechanical vibration of a permanent magnet linear motor. Control Eng Pract2008; 16: 787–797.
12.
AbdeljaberOAvciOInmanDJ.Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks. J Sound Vib2016; 363: 33–53.
13.
BuiH-LNguyenC-HBuiV-B, et al. Vibration control of uncertain structures with actuator saturation using hedge-algebras-based fuzzy controller. J Vib Control2017; 23: 1984–2002.
14.
EdalathSKukretiARCohenK.Enhancement of a tuned mass damper for building structures using fuzzy logic. J Vib Control2013; 19: 1763–1772.
15.
RădacM-BPrecupR-EPetriuEM, et al. Application of IFT and SPSA to servo system control. IEEE Trans Neural Networks2011; 22: 2363–2375.
16.
RemesCLGomesRBFloresJV, et al. Virtual reference feedback tuning applied to DC-DC converters. IEEE Trans Ind Electron2021; 68: 544–552.
17.
YahagiSKajiwaraIShimozawaT.Slip control during inertia phase of clutch-to-clutch shift using model-free self-tuning proportional-integral-derivative control. Proc IMechE, Part D: J Automobile Engineering2020; 234: 2279–2290.
18.
YonezawaHKajiwaraIYonezawaA.Experimental verification of model-free active vibration control approach using virtually controlled object. J Vib Control2020; 26: 1656–1667.
19.
YonezawaAYonezawaHKajiwaraI.Parameter tuning technique for a model-free vibration control system based on a virtual controlled object. Mech Syst Signal Process2022; 165: 108313.
20.
YonezawaAYonezawaHKajiwaraI.Vibration control for various structures with time-varying properties via model-free adaptive controller based on virtual controlled object and SPSA. Mech Syst Signal Process2022; 170: 108801.
21.
YonezawaAKajiwaraIYonezawaH.Model-free vibration control based on a virtual controlled object considering actuator uncertainty. J Vib Control2021; 27: 1324–1335.
22.
YonezawaAKajiwaraIYonezawaH.Novel sliding mode vibration controller with simple model-free design and compensation for actuator’s uncertainty. IEEE Access2021; 9: 4351–4363.
23.
YonezawaAYonezawaHKajiwaraI.Experimental verification of model-free vibration control technique based on a virtual controlled object considering actuator parameter uncertainty. In: Proceedings of the ASME 2021 international mechanical engineering congress and exposition, The American Society of Mechanical Engineers, 2021, p.V07AT07A039. New York: ASME.
24.
YangYMuñoaJAltintasY.Optimization of multiple tuned mass dampers to suppress machine tool chatter. Int J Mach Tools Manuf2010; 50: 834–842.
25.
PereiraEDíazIMHudsonEJ, et al. Optimal control-based methodology for active vibration control of pedestrian structures. Eng Struct2014; 80: 153–162.
26.
ZhouKDoyleJCGloverK.Robust and Optimal Control. Upper Saddle River New Jersey: PrenticeHall, 1996.
27.
ZhouKGloverKBodenheimerB, et al. Mixed H2 and H∞ performance objectives I: robust performance analysis. IEEE Trans Automat Contr1994; 39: 1564–1574.
28.
DoyleJZhouKGloverK, et al. Mixed H2 and H∞ performance objectives II: optimal control. IEEE Trans Automat Contr1994; 39: 1575–1587.
