Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

Baroud, Zakaria; Benmiloud, Mohammed; Benalia, Atallah; Ocampo‐Martínez, Carlos

doi:10.1016/j.ijhydene.2017.01.014

Cited by 140 publications

(67 citation statements)

References 37 publications

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“…The DC motor dynamics are neglected due to its small time constant compared to the mechanical system [33,34]. This model has been widely accepted by researchers for the design of the air feed system controller [16,35,36].…”

Section: Air Feed System Model For Pemfcmentioning

confidence: 99%

“…is the partial pressure of oxygen in the cathode, x 2 = P N 2 is the partial pressure of nitrogen in the cathode, x 3 = ω cp is the angular velocity of the compressor, x 4 = P sm is the pressure of the supply manifold, u = v cm is the compressor-motor voltage as the control input and ω = I st is the PEMFC stack current representing the measurable disturbance to the system. The components of f (x) are [16,35,37]:…”

Section: Air Feed System Model For Pemfcmentioning

confidence: 99%

“…Recently, soft computing techniques gained a lot of interest for the control of the PEMFC air feeding system. A B-spline neuro controller (B-SNN) was proposed in [14], PID feedback control with a single-input single-output (SISO) fuzzy feedforward controller [15] and hybrid fuzzy-PID controller [16].…”

Section: Introductionmentioning

confidence: 99%

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Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm

AbouOmar

Zhang

2019

Energies

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The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system.

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Section: Air Feed System Model For Pemfcmentioning

confidence: 99%

Section: Air Feed System Model For Pemfcmentioning

confidence: 99%

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Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm

AbouOmar

Zhang

2019

Energies

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“…Based on the values presented in Table , it can be concluded that the IAE, ITAE and ISE indices for the proposed strategy are less than one in . Although the settling times are approximately the same in both controllers, the percentage overshoot value in the IT2FPID is approximately 6 times less than the HFPID controller.…”

Section: Simulationmentioning

confidence: 99%

Control of PEM Fuel Cell Systems Using Interval Type‐2 Fuzzy PID Approach

Aliasghary

2018

Fuel Cells

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In this study, an interval type‐2 fuzzy proportional–integral–derivative (IT2FPID) controller is designed to be used in the air flow regulation of polymer electrolyte membrane fuel cell (PEMFC) systems. The proposed controller is able to handle the effect of external disturbances and fix the oxygen excess ratio at an appropriate set‐point. Three crisp singletons for the output and two interval type‐2 membership functions for the inputs are defined for the suggested controller. Two conventional controllers including type‐1 fuzzy PID (T1FPID) and ordinary proportional–integral–derivative (PID) are also investigated to evaluate the performance and efficiency of this technique. The results of various simulations indicate that the IT2FPID controller has a better performance and transient response compared to the other approaches.

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“…The classical solutions are the proportional-integral-derivative (PID) control [9] and its variants, e.g. The classical solutions are the proportional-integral-derivative (PID) control [9] and its variants, e.g.…”

Section: Introductionmentioning

confidence: 99%

Control‐oriented modeling and robust nonlinear triple‐step controller design for an air‐feed system for polymer electrolyte membrane fuel cells

Chen

Gong

et al. 2019

Asian Journal of Control

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The efficient operation of polymer electrolyte membrane fuel cells (PEMFCs) significantly relies on the reliable control of air-feed system. The core control objective in air-feed system is to track a pre-defined reference of the oxygen excess ratio to avoid oxygen starvation and stack damage. In this paper, we focus on the modeling of the air-feed system in a PEMFC and the robust nonlinear controller design for the oxygen excess ratio tracking control. To facilitate the subsequent nonlinear controller design, a specific affine-like, second-order, control-oriented model of oxygen excess ratio dynamic behavior is developed, and the modeling uncertainty is estimated and compensated by using an extended state observer (ESO). The control-oriented model is verified via a high-fidelity plant model. A nonlinear controller for oxygen excess ratio tracking control is proposed based on the triple-step technique which is robust against the system disturbances. The tuning rule of the controller parameters is discussed in the scheme of the linear system. Finally, simulations are conducted to demonstrate the effectiveness and advantages of the proposed controller under variant operating conditions compared with baseline controllers.

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Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

Cited by 140 publications

References 37 publications

Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm

Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm

Control of PEM Fuel Cell Systems Using Interval Type‐2 Fuzzy PID Approach

Control‐oriented modeling and robust nonlinear triple‐step controller design for an air‐feed system for polymer electrolyte membrane fuel cells

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