Multiple model predictive control for a hybrid proton exchange membrane fuel cell system

Chen, Qihong; Gao, Lijun; Dougal, Roger A.; Quan, Shuhai

doi:10.1016/j.jpowsour.2009.02.034

Cited by 91 publications

(33 citation statements)

References 14 publications

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“…This uncertainty appears at the time interval t = [10,20] s, as shown in Figure 18(b). It can be seen from Figure 18(a) that the HFPID controller exhibits a proper effect over this uncertainty.…”

Section: Sensibility Analysismentioning

confidence: 89%

“…In [14,15,16], different topologies of fuzzy-logic control (FLC) are proposed such as adaptive PID-based FLC, optimal PID plus fuzzy controller and feed-forward fuzzy PID. Other control strategies, as gain scheduled Linear Parameter-Varying (LPV) control [17], fault tolerant unfalsified control [18], Model Predictive Control (MPC) [19,20] and optimal control [21,22] were also reported to control the air supply PEMFC-based systems. All these control strategies are applied for the regulation of the oxygen excess ratio in the PEMFC with different degrees of success.…”

Section: Introductionmentioning

confidence: 99%

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

Baroud

Benmiloud

Benalia

et al. 2017

International Journal of Hydrogen Energy

150

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This paper proposes a novel hybrid fuzzy-PID controller for air supply on Proton Exchange Membrane fuel cell (PEMFC) systems. The control objective is to adjust the oxygen excess ratio at a given setpoint in order to prevent oxygen starvation and damage of the fuel-cell stack. The proposed control scheme consists of three parts: a fuzzy-logic controller (FLC), a fuzzy-based self-tuned PID (FSTPID) controller and a fuzzy selector. Depending on the value of the error between the current value of oxygen excess ratio and its setpoint value, the fuzzy selector decides which controller should play the greatest effect on the control system. The performance of the proposed control strategy is analysed through simulations for different load variations and for parameter uncertainties. The results show that the novel hybrid fuzzy-PID controller performs significantly better than the classical PID controller and the FLC in terms of several key performance indices such as the Integral Squared Error (ISE), the Integral Absolute Error (IAE) and the Integral Time-weighted Absolute Error (ITAE), as well as the overshoot, settling and rise time for the closed-loop control system.

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Section: Sensibility Analysismentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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

Baroud

Benmiloud

Benalia

et al. 2017

International Journal of Hydrogen Energy

150

View full text Add to dashboard Cite

show abstract

“…The main idea is to apply the basis function in PFC to the DGPC. The control input can be written as (20) according to the basis function. As the plant always has disturbances, we choose the basis function is slope function f(i) = i.…”

Section: Dgpc Based On Pfc Algorithmmentioning

confidence: 99%

“…The multi-model method based on divide-and-conquer strategy and is very popular for control industrial process, in which the dynamic properties changes with the operating conditions. Chen et al [20] proposed a multi-model predictive control method, and each sub-model corresponded to a MPC controller. The matching degree of sub-model is used to switch the MPC controller.…”

Section: Introductionmentioning

confidence: 99%

Multi‐model direct generalised predictive control for automatic train operation system

Wen

Yang

Rad

et al. 2015

IET Intelligent Transport Systems

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The authors propose a novel multi-model direct generalised predictive control based on predictive function control (PFC) algorithm for automatic train operation system. The proposed method facilitates autonomous driving of a train through a given guidance trajectory. Firstly, they present a multi-model architecture based on fuzzy c-means clustering algorithm. In order to obtain the optimal number of sub-linear models, they apply Xie-Beni cluster validity index. In this regards, the multi-model set is established off-line. Secondly, the proper sub-linear model is selected as the predictive model by using switching performance index at each time slot. The control variables are calculated by direct generalised predictive controller based on PFC. The control algorithm is simple, and can reduce the on-line computation time by directly identifies the unknown parameters in the controller. It can avoid recursively solving the Diophantine equations. The calculation of compensation value becomes simple by introducing PFC. Finally, simulation results are provided to show the effectiveness of the proposed scheme.

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“…To design predictive controller for the system, an objective function is defined as [18]

\begin{matrix} \min_{u (t), \dots, u (t + N - 1)} J = {false\sum}_{k = normal1}^{N_{u}} ({(\hat{y} (t + k) - y_{r} (t + k))}^{T} \\ \times Q (\hat{y} (t + k) - y_{r} (t + k)) \\ + u^{T} (t + k) R u^{T} (t + k)), \end{matrix}

where N u is predictive horizon,

\overset{y}{true} (t + k)

is the estimated output of the system at instant t + k through models based on information available at instant t . y r ( t + k ) is the desired output at instant t + k , and Q , R are weighting matrices on output errors and control, respectively.…”

Section: Controller Designmentioning

confidence: 99%

Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

Chen

Long

Quan

et al. 2014

The Scientific World Journal

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This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.

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Multiple model predictive control for a hybrid proton exchange membrane fuel cell system

Cited by 91 publications

References 14 publications

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

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

Multi‐model direct generalised predictive control for automatic train operation system

Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

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