Optimal Linear Parameter Varying Controller Design for Proton Exchange Membrane Fuel Cell Using LMI Techniques

Afsharinejad, Amir; Asemani, Mohammad Hassan; Dehghani, Maryam

doi:10.1109/icee50131.2020.9260820

Cited by 3 publications

(20 citation statements)

References 14 publications

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“…The linearized model is then represented by an LPV model. Inspired from [28], the LPV model of the system can be obtained as follows:…”

Section: Lpv Representationmentioning

confidence: 99%

“…However, the LPV approach [27] focuses on the non-optimal stabilization of the non-linear PEMFC system. Also, the LPV-based approach [28], considers stabilization of the non-linear PEMFC system with minimizing the membrane pressure difference of a PEM fuel cell. II.…”

Section: Remark 1 (Advantages Of the Proposed Approach)mentioning

confidence: 99%

“…Based on these selections, the controllers' gains are computed. Then, the six controllers, that is, Robust LPV (RoL) [27], LPV [46], optimal LPV with membrane pressure minimization (OPm) [28], optimal LPV with output voltage maximization (OPv), optimal LPV with membrane pressure minimization and output voltage maximization-Case 1 (OPmv1), and -Case 2 (OPmv2) are used to manipulate the PEMFC. Although the proposed approach, [27,46], and [28] are designed based on a polytopic-LPV representation, all controllers are applied to the non-linear PEMFC system.…”

Section: Proposed Controller Implementationmentioning

confidence: 99%

“…If the auxiliary parameter J * is minimized, then the cost function (27) is optimized. Applying the Schur complement on (33) results into the LMI (28). On the other hand, by replacing ( 23)-( 25) into (31), one obtains…”

Section: Optimal Controller Designmentioning

confidence: 99%

“…III. The represented polytopic-LPV model for the non-linear PEMFC stack dynamics comprises four vertices, which is far less than the one in [27] and [28], which provides a 32vertex polytopic-LPV model. The developed model results in considerably fewer LMIs and less conservative results than [27] and [28].…”

Section: Define a Coefficient-based Cost Function Comprising Two Auxi...mentioning

confidence: 99%

See 4 more Smart Citations

Optimal gain‐scheduling control of proton exchange membrane fuel cell: An LMI approach

Afsharinejad

Asemani

Dehghani

et al. 2021

IET Renewable Power Gen

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Here, an optimal linear parameter varying (LPV) controller is developed for non‐linear proton exchange membrane fuel cell (PEMFC) systems. Two performance criteria are involved in the optimal controller, including the membrane pressure and the power of the PEMFC. The former should be minimized; and, the latter should be maximized. Since the goal is to derive sufficient conditions in terms of linear matrix inequality (LMI) constraints, both performances are re‐formulated and augmented in a unique convex optimization problem. Further, the non‐linear PEMFC with the equilibrium profile dependent on time‐varying parameters, that is, current and temperature, is represented by an LPV model. Using the LMI constraints and the obtained LPV model, the gains of the controller are derived. Comparing with the state‐of‐the‐art methods, the proposed approach offers an optimal control approach with a systematic design method, in which both practical issues are treated, simultaneously. To show the advantages of the developed approach, six typical controllers, including the proposed and existing approaches, are considered and several numerical simulation results are conducted.

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“…The linearized model is then represented by an LPV model. Inspired from [28], the LPV model of the system can be obtained as follows:…”

Section: Lpv Representationmentioning

confidence: 99%

Section: Remark 1 (Advantages Of the Proposed Approach)mentioning

confidence: 99%

Section: Proposed Controller Implementationmentioning

confidence: 99%

Section: Optimal Controller Designmentioning

confidence: 99%

Section: Define a Coefficient-based Cost Function Comprising Two Auxi...mentioning

confidence: 99%

See 3 more Smart Citations

Optimal gain‐scheduling control of proton exchange membrane fuel cell: An LMI approach

Afsharinejad

Asemani

Dehghani

et al. 2021

IET Renewable Power Gen

Self Cite

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Advanced Control of DC Grid-Connected Proton Exchange Membrane Fuel Cell: A Linear Parameter Varying Approach

Afsharinejad

Dehghani

Asemani

et al. 2021

2021 International Conference on Smart Energy Systems and Technologies (SEST)

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Distributed Intelligence in Autonomous PEM Fuel Cell Control

Rubio,

Agila,

González

et al. 2023

Energies

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A combination of perceptive and deliberative processes is necessary to ensure the efficient and autonomous control of proton exchange membrane fuel cells (PEMFCs) under optimal humidification conditions. These processes enable monitoring and control tasks across various application scenarios and operating conditions. Consequently, it becomes crucial to adjust parameter values corresponding to different states of the PEMFC during its operation. In this context, this work presents the design and development of an architecture for the control and management of a PEMFC with a maximum power output of 500 [W] based on intelligent agents operating under optimal conditions (membrane humidification). The proposed architecture integrates perception and action algorithms that leverage sensory and contextual information using heuristic algorithms. It adopts a hierarchical structure with distinct layers, each featuring varying time windows and levels of abstraction. Notably, this architecture demonstrates its effectiveness in achieving the desired energy efficiency objective, as evidenced by successful validation tests conducted with different electrical power values delivered by the fuel cell, encompassing three distinct operating states (dry, normal, and flooded). An exemplary application of this scheme is the dynamic control of the humidification of the polymeric membrane, which further highlights the capabilities of this architecture.

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Optimal Linear Parameter Varying Controller Design for Proton Exchange Membrane Fuel Cell Using LMI Techniques

Cited by 3 publications

References 14 publications

Optimal gain‐scheduling control of proton exchange membrane fuel cell: An LMI approach

Optimal gain‐scheduling control of proton exchange membrane fuel cell: An LMI approach

Advanced Control of DC Grid-Connected Proton Exchange Membrane Fuel Cell: A Linear Parameter Varying Approach

Distributed Intelligence in Autonomous PEM Fuel Cell Control

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