Based on vehicle-to-grid technology, electric vehicles can be used as power sources in the case of power failure. With the aim to reduce voltage overshoot and improve the anti-disturbance ability of the vehicle-to-grid inverter, a high-performance voltage control strategy based on funnel control and finite-time disturbance observer is developed. First, the dynamic model of the inverter in dq-frame is established, and the lumped disturbance including the unmodeled part is considered. Next, a novel funnel variable is proposed to ensure that the voltage tracking error can be stabilized within the prescribed funnel boundary, and thus enhance the transient performance. Then, a novel finite-time disturbance observer is designed to estimate the lumped disturbance in the system such as load fluctuations, and improve the anti-disturbance ability of the controller. Moreover, the second-order sliding mode differentiator is introduced to estimate the derivative of the virtual control law and eliminate the explosion of complexity problem in the derivation process. Finally, the finite-time stability of the proposed voltage control strategy is analyzed via the Lyapunov theory. The effectiveness of the proposed control strategy is verified by two cases.
RenYDinhTQMarcoJ, et al. Torque vectoring-based drive: assistance system for turning an electric narrow tilting vehicle. Proc IMechE, Part I: J Systems and Control Engineering2019; 233(7): 788–800.
2.
ZhaoXYinLFuB, et al. An improved maximum power point tracking control approach for photovoltaic systems based on model predictive control with a condition-triggered strategy. Proc IMechE, Part I: J Systems and Control Engineering2016; 230(3): 277–288.
3.
LaustsenSLundEKhlmeierL, et al. Failure behaviour of grid-scored foam cored composite sandwich panels for wind turbine blades subjected to realistic multiaxial loading conditions. J Sandwich Struct Mater2014; 16(5): 481–510.
4.
WangZWangS.Grid power peak shaving and valley filling using vehicle-to-grid systems. IEEE Trans Power Delivery2013; 28(3): 1822–1829.
5.
GaoZJiangBShiP, et al. Active fault-tolerant tracking control for near-space vehicle attitude dynamics with actuator faults. Proc IMechE, Part I: J Systems and Control Engineering2011; 225(3): 413–422.
6.
LiFHGuoZJingZ, et al. Peak and valley regulation of distribution network with electric vehicles. J Eng2019; 16: 2488–2492.
7.
TalGNicholasM.Exploring the impact of the federal tax credit on the plug-in vehicle market. Transport Res Record2016; 2572(1): 95–102.
8.
XuDXuAYangC, et al. Uniform state-of-charge control strategy for plug-and-play electric vehicle in super-UPS. IEEE Trans Transport Electrific2019; 5(4): 1145–1154.
9.
DuZCheongKLLiPY.Energy management strategy for a power-split hydraulic hybrid vehicle based on Lagrange multiplier and its modifications. Proc IMechE, Part I: J Systems and Control Engineering2019; 233(5): 511–523.
10.
MengQHouZ.Data-driven multi-inverter cooperative control for voltage tracking and current sharing in islanded AC microgrids. Trans Institute Measure Contr2019; 41(11): 3145–3157.
11.
AlobeidliKASyedMHEl MoursiMS, et al. Novel coordinated voltage control for hybrid micro-grid with islanding capability. IEEE Trans Smart Grid2015; 6(3): 1116–1127.
12.
DelghaviMShoja-MajidabadSYazdaniA, et al. Fractional-order sliding-mode control of islanded distributed energy resource systems. IEEE Trans Sustain Energy2016; 7(4): 1482–1491.
13.
XiaYDaiYYanW, et al. Adaptive-observer-based data driven voltage control in islanded-mode of distributed energy resource systems. Energies2018; 11(12): 1–14.
14.
ZhengLJiangFSongJ, et al. A discrete-time repetitive sliding mode control for voltage source inverters. IEEE J Emerg Select Topics Power Electron2018; 6(3): 1553–1566.
15.
WangXYinXShenF, et al. Disturbance observer based adaptive neural prescribed performance control for a class of uncertain nonlinear systems with unknown backlash-like hysteresis. Neurocomputing2018; 299(19): 10–19.
16.
NiJAhnCKLiuL, et al. Prescribed performance fixed-time recurrent neural network control for uncertain nonlinear systems. Neurocomputing2019; 363(21): 351–365.
17.
ZhangLYangG.Adaptive fuzzy prescribed performance control of nonlinear systems with hysteretic actuator nonlinearity and faults. IEEE Trans Syst Man Cybernet Syst2018; 48(12): 2349–2358.
18.
ShaoXHuQShiY, et al. Fault-tolerant prescribed performance attitude tracking control for spacecraft under input saturation. IEEE Trans Contr Syst Technol2020; 28(2): 574–582.
19.
ZhangTLiJLiW, et al. Prescribed performance neural control to guarantee tracking quality for near space kinetic kill vehicle. J Syst Eng Electron2019; 30(3): 573–586.
20.
WangSYuHYuJ, et al. Neural-network-based adaptive funnel control for servo mechanisms with unknown dead-zone. IEEE Trans Cybernet2020; 50(4): 1383–1394.
21.
BergerTReisT.Funnel control via funnel precompensator for minimum phase systems with relative degree two. IEEE Trans Automat Contr2018; 63(7): 2264–2271.
22.
WangSRenXNaJ, et al. Extended-state-observer-based funnel control for nonlinear servomechanisms with prescribed tracking performance. IEEE Trans Automat Sci Eng2017; 14(1): 98–108.
23.
LiuCWangHLiuX, et al. Adaptive finite-time fuzzy funnel control for nonaffine nonlinear systems. IEEE Trans Syst Man Cybernet Syst2020; 51: 2894–2903.
24.
BuX.Air-breathing hypersonic vehicles funnel control using neural approximation of non-affine dynamics. IEEE/ASME Trans Mechatron2018; 23(5): 2099–2108.
25.
AliMYaqoobMCaoL, et al. Disturbance-observer-based dc-bus voltage control for ripple mitigation and improved dynamic response in two-stage single-phase inverter system. IEEE Trans Indus Electron2019; 66(9): 6836–6845.
26.
WangYYuHLiuLY.Speed-current single-loop control with overcurrent protection for PMSM based on time-varying nonlinear disturbance observer. IEEE Trans Indus Electron. Epub ahead of print 20 January 2021. DOI: 10.1109/TIE.2021.3051594.
27.
YuSYuXShirinzadehB, et al. Continuous finite-time control for robotic manipulators with terminal sliding mode. Automatica2005; 41(11): 1957–1964.
28.
LevantA.Higher-order sliding modes, differentiation and output-feedback control. Int J Contr2003; 76(9–10): 924–941.
29.
HuangXLinWYangB, et al. Global finite-time stabilization of a class of uncertain nonlinear systems. Automatica2005; 41(5): 881–888.
30.
YuJShiPZhaoL, et al. Finite-time command filtered backstepping control for a class of nonlinear systems. Automatica2018; 92: 173–180.
