Nonlinear control of air-feed system for proton exchange membrane fuel cell with auxiliary power battery

Zhang, Fan; Ma, Yan; Zhu, Tianlin; Chen, Hong

doi:10.1063/1.5093984

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…In most of the existing results on the primary control of power components connected to a microgrid, the control law for each power electronic converters is constructed based on the states of its corresponding component [21][22][23].…”

Section: ⎧ ⎨ ⎩ (31)mentioning

confidence: 99%

“…en, the polytopic-LPV controller design procedure for the system is designed such that the exponential stability of the overall system is assured. e proposed approach has some advantages over state-of-the-art methods, including the following: (1) it considers the available information of all components in the design procedure compared to [21][22][23]. erefore, the overall closed-loop stability is guaranteed theoretically through the Lyapunov stability theory [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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A Linear Parameter Varying Control Approach for DC/DC Converters in All‐Electric Boats

et al. 2021

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Utilization of renewable energies in association with energy storage is increased in different applications such as electrical vehicles (EVs), electric boats (EBs), and smart grids. A robust controller strategy plays a significant role to optimally utilize the energy resources available in a power system. In this paper, a suitable controller for the energy resources of an EB which consists of a 5 kW solar power plant, 5 kW fuel cell, and 2 kW battery package is designed based on the linear parameter varying (LPV) controller design approach. Initially, all component dynamics are augmented, and by exploiting the sector-nonlinearity approach, the LPV representation is derived. Then, the LPV control method determines the suitable gains of the states’ feedbacks to provide the required pulse commands of the boost converters of the energy resources to regulate the DC-link voltage and supply the power of EB loads. Comparing with the state-of-the-art nonlinear control methods, the developed control approach assures the stability of the overall system, as it considers all component dynamics in the design procedure. The real-time simulation results demonstrate the performance of the designed controller in the creation of a constant DC-link voltage.

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Section: ⎧ ⎨ ⎩ (31)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Linear Parameter Varying Control Approach for DC/DC Converters in All‐Electric Boats

et al. 2021

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“…• The proposed controller deploys the information of all distributed generators, battery packet, and DC bus in the design procedure compared to [24]- [26], which uses the information of each generator to control it. Thereby, the closed-loop stability is guaranteed theoretically based on the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Linear Parameter-Varying Control of Shipboard Direct Current Microgrids

et al. 2022

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To avoid pollution of transportation applications, renewable energies are deployed. Whereas they are uncontrolled, fully controlled pollution-free energy sources and storage units should be also considered. However, a complicated direct current (DC) microgrid (MG) is obtained, which suffers from nonlinearities, high order, and uncertainties of the power elements. Therefore, it is vital to use advanced nonlinear controllers to assure closed-loop stability and performance of the overall system. In this paper, a neural network (NN)-based adaptive linear parameter varying (LPV) controller is suggested for the whole DC MG power system. The main advantage of the developed controller is that it deploys the operating information of all power energy sources to manipulate each component. This enhances the DC MG stability margin and fast regulation of the power and voltage. Besides, the proposed controller has a systematic and fully offline design algorithm by combining numerical solvers and theoretical theories. The LPV controller is designed via the numerical linear matrix inequality (LMI) approach and the adaptation law for the NN parameters is designed based on the Lyapunov stability theory. A DC MG benchmark including a 5kW fuel cell, a 5kW solar plant, and a 2kW battery package is considered as the case study, which can be utilized in future fully electric boats (EBs). Numerical simulations with different scenarios are conducted to verify the performance of the proposed controller. Furthermore, comparative results are provided to show the advantages of the proposed method dealing with power fluctuations of the solar plant and DC loads over state-of-the-art nonlinear methods.

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Model-free adaptive control for the PEMFC air supply system based on interval type-2 fuzzy logic systems

Luo

Wang

et al. 2020

Journal of Renewable and Sustainable Energy

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Control aims to avoid oxygen starvation and maximize the net power output by maintaining the optimal oxygen excess ratio (OER), which varies between 1.8 and 2.5. Because of the nonlinearity of the proton exchange membrane fuel cell (PEMFC) air supply system and the different conditions, ensuring an optimal OER is still a challenge. In this study, a model-free adaptive controller is presented for the PEMFC air supply system based on feedback linearization and interval type-2 fuzzy logic systems (IT2 FLSs). Theoretical analysis and experimental results verify the effectiveness of the proposed control scheme. For the theoretical analysis, first, the PEMFC air supply system is transformed into a canonical form with the feedback linearization technique. Then, zero-dynamics stability is discussed in detail to determine the stability of the internal dynamics. Finally, an adaptive interval type-2 fuzzy logic system controller (AIT2FLSC) is designed on the basis of the Lyapunov stability theory, which does not require complete a priori knowledge of the system dynamics. For the experimental results, the root mean square error (RMSE), variance, and standard deviation (SD) of the tracking error are used as tracking performance metrics to evaluate the control accuracy of the proposed AIT2FLSC, which are 0.0968, 0.0093, and 0.0962, respectively. Compared with the traditional proportion integration differentiation controller (RMSE 0.1119, variance 0.0122, and SD 0.1105), this proposed algorithm obtains better adaptability and the RMSE of the tracking error improves 13.48%. Compared with the adaptive type-1 fuzzy logic system controller (AT1FLSC) (RMSE 0.1076, variance 0.0113, and SD 0.1063), this AT2FLSC has a stronger ability to deal with uncertainty and the RMSE of the tracking error improves 10% when the stack temperature is fixed (353.15 K). Furthermore, when the stack temperature is time-varying, the RMSE, variance, and SD of the tracking error under the AIT2FLSC are 0.0966, 0.0092, and 0.0960, respectively, which is less than AT1FLSC (0.1085, 0.0115, and 0.1073) and the RMSE of the tracking error improves 10.99%.

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Nonlinear control of air-feed system for proton exchange membrane fuel cell with auxiliary power battery

Cited by 5 publications

References 23 publications

A Linear Parameter Varying Control Approach for DC/DC Converters in All‐Electric Boats

A Linear Parameter Varying Control Approach for DC/DC Converters in All‐Electric Boats

Adaptive Neural Network Linear Parameter-Varying Control of Shipboard Direct Current Microgrids

Model-free adaptive control for the PEMFC air supply system based on interval type-2 fuzzy logic systems

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