Robust Model-Based Fault Diagnosis for PEM Fuel Cell Air-Feed System

Liu, Jianxing; Luo, Wenshu; Yang, Xiaozhan; Wu, Ligang

doi:10.1109/tie.2016.2535118

Cited by 310 publications

(93 citation statements)

References 40 publications

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“…First, we let (11) and (12), it derives that the conditions (7) and (8) hold. That is to say system (6) is very strictly passive if equations (11) and (12) hold. This ends the proof.…”

Section: Resultsmentioning

confidence: 99%

Memory-based passive control design for complex networked fuzzy delta operator systems

Zhao

Yan

Shen

2017

Advances in Mechanical Engineering

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This article investigates the memory-based state-feedback control with strict passivity performance for nonlinear systems in delta domain. Takagi-Sugeno fuzzy model of delta operator form is adopted as an approximator of the investigated nonlinear plant with system perturbations/exogenous disturbances. The nonlinear output of the plant, which is accompanied with complex exogenous disturbances, is formulated as the passive output satisfying a pre-designed strict passivity performance. The memory-based state-feedback controller is expressed with a known memory coefficient to significant the influence of the delayed states. The resulting closed-loop system is inferred as a time-varying delay system, of which the stability and passivity criterion is carried out via a general fuzzy Lyapunov-Krasovskii functional approach. Then, parametric gains of the desired strictly passive memory-based controller are designed subject to a set of solvable matrix inequalities. Finally, a numerical example shows the validity of the proposed memory-based passive control method.

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“…First, we let (11) and (12), it derives that the conditions (7) and (8) hold. That is to say system (6) is very strictly passive if equations (11) and (12) hold. This ends the proof.…”

Section: Resultsmentioning

confidence: 99%

Memory-based passive control design for complex networked fuzzy delta operator systems

Zhao

Yan

Shen

2017

Advances in Mechanical Engineering

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“…From the fact that V H (k) is positive definite, V H (k) 0 for all of k, ∆V H (k) is negative definite when the η H satisfies (20). Therefore, it follows that lim k→∞ê (k) = 0.…”

Section: B Hammerstein-type Neural Network Identificationmentioning

confidence: 99%

“…Among the above technologies, the fuel cell has the characteristics of low operating temperature, quick start, good stability, no radiation and air pollution [18][19][20]. However, the dynamic response of fuel cell has a certain delay.…”

mentioning

confidence: 99%

A Novel Adaptive Neural Network Constrained Control for a Multi-Area Interconnected Power System With Hybrid Energy Storage

Liu

Yan

et al. 2018

IEEE Trans. Ind. Electron.

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172

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Abstract-This paper concentrates on the problem of control of a hybrid energy storage system (HESS) for an improved and optimized operation of load-frequency control (LFC) applications. The HESS consists of a supercapacitor served as the main power source, and a fuel cell served as the auxiliary power source. Firstly, a Hammerstein-type neural network (HNN) is proposed to identify the HESS system, which formulates the Hammerstein model with a nonlinear static gain in cascade with a linear dynamic block. It provides the model information for the controller to achieve the adaptive performance. Secondly, a feedforward neural network based on back-propagation training algorithm is designed to formulate the PID-type neural network (PIDNN), which is used for the adaptive control of HESS system. Meanwhile, a dynamic anti-windup signal is designed to solve the operational constraint of the HESS system. Then, an appropriate power reference signal for HESS can be generated. Thirdly, the stability and the convergence of the whole system are proved based on the Lyapunov stability theory. Finally, simulation experiments are followed through on a four-area interconnected power system to demonstrate the effectiveness of the proposed control scheme.Index Terms-Load-frequency control (LFC), hybrid energy storage system (HESS), Hammerstein network identification, PIDtype neural network (PIDNN), dynamic anti-windup, adaptive control

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“…Furthermore, the aforementioned methods for linear networked control systems cannot be used more directly. In the light of nonlinear characteristics, a few classes of advanced techniques containing sliding mode control [21], adaptive control [22,23], and fuzzy control [24,25] were applied. The tracking control of network control system involves some nonlinear factors, such as bandwidth constraints, packet dropouts, and network delays.…”

Section: Introductionmentioning

confidence: 99%

H∞ Tracking Control of Fuzzy Dynamic Output for Nonlinear Networked System with Packet Dropouts

Wang

2018

Mathematical Problems in Engineering

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The tracking control of ∞ dynamic output feedback is proposed for the fuzzy networked systems of the same category, in which each system is discrete-time nonlinear and is missing measurable data. In other words, the loss of data packet occurs randomly in both the uplink and the downlink. The independent variables that are called the Bernoulli random variables are considered to design the loss of data packets. The method of parallel distributed compensation (PDC) in terms of the T-S fuzzy model is applied to investigate the dynamic controller of tracking control on the systems. Then, it is presented that the analytical ∞ performance of the output error between the reference model and the fuzzy model for the closed-loop system containing dynamic output feedback controller is proven. Furthermore, the achieved sufficient conditions in terms of LMIs ensure that the closed-loop system is stochastically stable in the ∞ sense. Finally, a numerical system is offered to show the effectiveness of the established technique.

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Robust Model-Based Fault Diagnosis for PEM Fuel Cell Air-Feed System

Cited by 310 publications

References 40 publications

Memory-based passive control design for complex networked fuzzy delta operator systems

Memory-based passive control design for complex networked fuzzy delta operator systems

A Novel Adaptive Neural Network Constrained Control for a Multi-Area Interconnected Power System With Hybrid Energy Storage

H∞ Tracking Control of Fuzzy Dynamic Output for Nonlinear Networked System with Packet Dropouts

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