ARX modelling and model predictive control for solid oxide fuel cell

Huo, Haibo; Huo, Haie; Chen, Yunlan; Kuang, Xinghong; Li, Junjun; Wu, Yanxiang

doi:10.1504/ijmic.2013.055915

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…To control the SOFC system, there exist many different methods. The model predictive control (MPC) which is the widely used one, was considered in [15,16]. In this paper, we have proposed a control strategy that consists of a predictive control based on a nonlinear auto-regressive model with exogenous input (NARX) fuzzy dynamic model.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Model Feed Forward Predictive Control of the Nonlinear Tubular Solid-Oxide Fuel Cell System

Ouagueni¹,

Boumehraz²,

Belhamdi³

2019

AMA_C

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The solid oxide fuel cell (SOFC) is broadly used for distributed and clean power generation. The main problem related to SOFC lies in the difficulties to control the output voltage of the SOFC due to the strong nonlinearity, the rapid changes of the load and the limited fuel flow. The objective of the control of the SOFC system is to maintain the output voltage at a constant level and the fuel utilization rate in a safety interval. In this context, a multiple-input multiple-output (MIMO) discrete-time Takagi-Sugeno (TS) fuzzy dynamic model with feed forward input is used in this paper to describe the dynamic properties of the nonlinear voltage and the fuel utilization rate in a tubular SOFC system. This obtained fuzzy model will be used for the application of constrained fuzzy model predictive control. The simulation results are provided to show the accuracy and the effectiveness of the proposed strategy.

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Section: Introductionmentioning

confidence: 99%

A Fuzzy Model Feed Forward Predictive Control of the Nonlinear Tubular Solid-Oxide Fuel Cell System

Ouagueni¹,

Boumehraz²,

Belhamdi³

2019

AMA_C

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“…It integrates therefore easily in the standard formulation of problems control estimation. The control structures based on the MLD formalism are essentially based on optimisation techniques for which we seek to minimise a linear or quadratic cost function, especially using the model predictive control (MPC) (Vinodha et al, 2011;Huo et al, 2013). Those techniques can be solved through mixed integer linear or quadratic programming (MILP/MIQP), for which efficient solvers are available.…”

Section: Introductionmentioning

confidence: 99%

Modelling and predictive control of an inverted pendulum system by MLD approach: multivariable case

Saidi

Hammi

Douik

2017

IJMIC

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This paper deals with a class of hybrid systems that is presented by mixed logical dynamical (MLD) formalism. This approach is well-adapted to hybrid systems, particularly because of its ability not only to model systems involving continuous and discrete dynamics under constraints, but also to address several issues such as model predictive control (MPC) of this class of systems. This approach will be illustrated by the multivariable modelling of the inverted pendulum system. Therefore, the resultant system is well-posed for MPC strategy. Simulations were effected using the HYSDEL compiler to illustrate the efficiency of this formalism.

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“…It requires a high amount of calculations, but the fast microprocessors available today make possible the implementation of predictive control. Several control algorithms have been presented under the name of predictive control, as presented in Corts et al (2008Corts et al ( , 2009, Huo et al (2013) and Jiang et al (2013). A well-known type of these control schemes is model predictive control (MPC).…”

Section: Introductionmentioning

confidence: 99%

Improved model predictive control for three-phase inverter with output LC filter

Mohamed¹,

Zaid

Abu-Elyazeed

et al. 2015

IJMIC

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The control of inverters with output LC filter has a special importance in applications where a high quality voltage is needed. However, the controller design becomes more difficult. A model predictive control (MPC) is used for voltage control of a three-phase inverter with output LC filter. The controller uses a model of the system to predict the behaviour of the variables for a given voltage vector sequence until a certain horizon of time, then a cost function is used as a criterion for selecting the switching state that will be applied during the next sampling interval. This paper presents the effect of considering different number of prediction steps in terms of THD and the number of cycles or the settling time to reach steady state operation. The simulation results for MPC with only one prediction step and the improved MPC with two prediction steps are presented and compared, under linear and nonlinear loads, using MATLAB/Simulink tools. The simulation results show that the improved MPC improves the THD for nonlinear loads and make it constant for different resistive loads. Moreover, the settling time can be considered constant for various linear and nonlinear loads.

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ARX modelling and model predictive control for solid oxide fuel cell

Cited by 4 publications

References 20 publications

A Fuzzy Model Feed Forward Predictive Control of the Nonlinear Tubular Solid-Oxide Fuel Cell System

A Fuzzy Model Feed Forward Predictive Control of the Nonlinear Tubular Solid-Oxide Fuel Cell System

Modelling and predictive control of an inverted pendulum system by MLD approach: multivariable case

Improved model predictive control for three-phase inverter with output LC filter

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