A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup

Ji, Nan; Xu, Dazhuan; Liu, Fei

doi:10.1016/j.neucom.2016.05.076

Cited by 31 publications

(16 citation statements)

References 38 publications

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“…To solve the problem of maintaining the output voltage and fuel utilization of SOFCs, many advanced control strategies have been proposed, such as model predictive control (MPC) [11][12][13], fuzzy proportional-integral-derivative (PID) control [14], fuzzy logic control [15], and neural network control [16]. All of these control strategies have been shown to obtain excellent control performance in numerous simulation studies: However, due to their computational complexity, these control strategies are difficult to implement in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

et al. 2019

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Nowadays, given the great deal of fossil fuel consumption and associated environmental pollution, solid oxide fuel cells (SOFCs) have shown their great merits in terms of high energy conversion efficiency and low emissions as a stationary power source. To ensure power quality and efficiency, both the output voltage and fuel utilization of an SOFC should be tightly controlled. However, these two control objectives usually conflict with each other, making the controller design of an SOFC quite challenging and sophisticated. To this end, a multi-objective genetic algorithm (MOGA) was employed to tune the proportional–integral–derivative (PID) controller parameters through the following steps: (1) Identifying the SOFC system through a least squares method; (2) designing the control based on a relative gain array (RGA) analysis; and (3) applying the MOGA to a simulation to search for a set of optimal solutions. By comparing the control performance of the Pareto solutions, satisfactory control parameters were determined. The simulation results demonstrated that the proposed method could reduce the impact of disturbances and regulate output voltage and fuel utilization simultaneously (with strong robustness).

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Section: Introductionmentioning

confidence: 99%

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

et al. 2019

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“…The SOFC dynamics model has been widely accepted widely is used as the research model [18][19]. An improved data driven model-free adaptive constrained control [18] has been used to maintain the output voltage of SOFC V S at 333V.…”

Section: ) Mode Of Fuel Cellmentioning

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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“…In order to eliminate the output non-following problem produced by the actuator saturation, an integral SMC based on antiwindup compensator is proposed [28]. Define the velocity tracking error as…”

Section: Model-free Adaptive Smc Design and Stability Analysismentioning

confidence: 99%

“…Since lies in the unit disk and assuming ( ) − ( − 1) is bounded, the signal ( ) is uniformly ultimately bounded (UUB) for all according to [28].…”

Section: Theorem 7 When the Integral Sliding-mode Surface Is Boundedmentioning

confidence: 99%

Improved Model-Free Adaptive Sliding-Mode-Constrained Control for Linear Induction Motor considering End Effects

Song

Yang

et al. 2018

Mathematical Problems in Engineering

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As a kind of special motors, linear induction motors (LIM) have been an important research field for researchers. However, it gives a great velocity control challenge due to the complex nonlinearity, high coupling, and unique end effects. In this article, an improved model-free adaptive sliding-mode-constrained control method is proposed to deal with this problem dispensing with internal parameters of the LIM. Firstly, an improved compact form dynamic linearization (CFDL) technique is used to simplify the LIM plant. Besides, an antiwindup compensator is applied to handle the problem of the actuator under saturations in case during the controller design. Furthermore, the stability of the closed system is proved by Lyapunov stability method theoretically. Finally, simulation results are given to demonstrate that the proposed controller has excellent dynamic performance and stronger robustness compared with traditional PID controller.

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A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup

Cited by 31 publications

References 38 publications

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

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

Improved Model-Free Adaptive Sliding-Mode-Constrained Control for Linear Induction Motor considering End Effects

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