Adaptive Neural Network Sliding Mode Control for the Fuel Cell Air Supply System

doi:10.12068/j.issn.1005-3026.2022.09.008

Abstract

Abstract: The air supply system of polymer electrolyte membrane fuel cell(PEMFC) is vulnerable to the negative impact of parameter uncertainties and external disturbances， and it is difficult to achieve high-precision mathematical modeling and robust control. An adaptive neural network sliding mode controller is designed to adjust the oxygen excess ratio of PEMFC air supply system to its optimal reference value so as to maintain the maximum system output net power and avoid oxygen starvation. The radial basis function(RBF) neural network is employed to approximate the dynamics of the unmodeled system online without prior information of the boundary of external disturbances and model parameter perturbations. To ensure the stability of the closed-loop system， the adaptive laws of neural network weights and sliding mode gains are derived by Lyapunov theory. The numerical simulation results demonstrate that the designed controller not only improves the dynamic behavior of the oxygen excess ratio control， but also effectively alleviates the large overshoot and chattering of the control input.

Key words: polymer electrolyte membrane fuel cell(PEMFC); oxygen excess ratio; radial basis function(RBF)neural network; sliding mode control; adaptive law

CLC Number:

TP29

ZHANG Chun-lei， LI He， DONG Mao-lin， ZHANG Sheng-jie. Adaptive Neural Network Sliding Mode Control for the Fuel Cell Air Supply System[J]. Journal of Northeastern University(Natural Science), 2022, 43(9): 1270-1276.

References

[1]Abbaker A M O，Wang H，Tian Y.Adaptive integral type-terminal sliding mode control for PEMFC air supply system using time delay estimation algorithm［J］.Asian Journal of Control，2022，24(1):217-226.
[2]Deng H，Li Q，Cui Y，et al.Nonlinear controller design based on cascade adaptive sliding mode control for PEM fuel cell air supply systems［J］.International Journal of Hydrogen Energy，2019，44(35):19357-19369.
[3]Ou K，Wang Y X，Li Z Z，et al.Feed forward fuzzy-PID control for air flow regulation of PEM fuel cell system［J］.International Journal of Hydrogen Energy，2015，40(35):11686-11695.
[4]Pukrushpan J T，Stefanopoulou A G，Peng H.Control of fuel cell breathing［J］.IEEE Control Systems，2004，2(24):30-46.
[5]Talj R J，Ortega R，Astolfi A.Passivity and robust PI control of the air supply system of a PEM fuel cell model［J］.Automatica，2011，47(12):2554-2561.
[6]Abdullah M，Idres M.Constrained model predictive control of proton exchange membrane fuel cell［J］.Journal of Mechanical Science and Technology，2014，28(9):3855-3862.
[7]Chen J，Liu Z Y，Wang F，et al.Optimal oxygen excess ratio control for PEM fuel cells［J］.IEEE Transactions on Control Systems Technology，2018，26(5):1711-1721.
[8]Baroud Z，Benmiloud M，Benalia A，et al.Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems［J］.International Journal of Hydrogen Energy，2017，42(15):10435-10447.
[9]Ahmed S，Wang H P，Tian Y.Model-free control using time delay estimation and fractional-order nonsingular fast terminal sliding mode for uncertain lower-limb exoskeleton［J］.Journal of Vibration and Control，2018，24(22):5273-5290.
[10]Javaid U，Mehmood A，Arshad A，et al.Operational efficiency improvement of PEM fuel cell—a sliding mode based modern control approach［J］.IEEE Access，2020，8:95823-95831.
[11]Naghmeh M，Mehdi R S，Mahmood G.Robust control design for air breathing proton exchange membrane fuel cell system via variable gain second-order sliding mode［J］.Energy Science & Engineering，2018，6(3):126-143.
[12]Kunusch C，Puleston P F，Mayosky M A，et al.Sliding mode strategy for PEM fuel cells stacks breathing control using a super-twisting algorithm［J］.IEEE Transactions on Control Systems Technology，2009，17(1):167-174.
[13]Matraji I，Laghrouche S，Jemei S，et al.Robust control of the PEM fuel cell air-feed system via sub-optimal second order sliding mode［J］.Applied Energy，2013，104:945-957.
[14]Pilloni A，Pisano A，Usai E .Observer-based air excess ratio control of a PEM fuel cell system via high-order sliding mode［J］.IEEE Transactions on Industrial Electronics，2015，62(8):5236-5246.
[15]Wang J，Liu Y，Cao G，et al.Design of RBF adaptive sliding mode controller for a super cavitating vehicle［J］.IEEE Access，2021，9:39873-39883.
[16]Wang Y L，Wang Y F，Chen G.Robust composite adaptive neural network control for air management system of PEM fuel cell based on high-gain observer［J］.Neural Computing and Applications，2020，32(14):10229-10243.
[17]Talj R J，Hissel D，Ortega R，et al.Experimental validation of a PEM fuel cell reduced order model and a moto-compressor higher order sliding mode control［J］.IEEE Transactions on Industrial Electronics，2010，57(6):1906-1913.
[18]Li T，Duan S，Liu J，et al.An improved design of RBF neural network control algorithm based on spintronic memristor crossbar array［J］.Neural Computing & Applications，2016，30:1939-1946.
[19]Wang H，He P，Yu M，et al.Adaptive neural network sliding mode control for steer-by-wire-based vehicle stability control［J］.Journal of Intelligent & Fuzzy Systems，2016，31(2):885-902.
[20]Nasiri A，Nguang S K，Swain A.Adaptive sliding mode control for a class of MIMO nonlinear systems with uncertainties［J］.Journal of the Franklin Institute，2014，351(4):2048-2061.
[21]Qin H D，Yu X，Zhu Z B，et al.An expectation-maximization based single-beacon underwater navigation method with unknown ESV［J］.Neurocomputing，2019，378:295-303.