Modeling, stability analysis, and computational aspects of some simplest nonlinear fuzzy two-term controllers derived via center of area/gravity defuzzification

Arun, N.; Mohan, B.M.

doi:10.1016/j.isatra.2017.04.023

Cited by 40 publications

(13 citation statements)

References 40 publications

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“…In literature, the combination of the aforementioned controllers such as PI-Fuzzy control and Fuzzy SMC and some other types are suggested in different studies and similar results are obtained (Arif , 2020;Ghabi et al, 2018). Arun andMohan (2017), Fang, Shen, andFeng (2009) suggest a new nonlinear fuzzy controller to control and track the flow level in the coupled tank system. In other research work Souran, Abbasi, and Shabaninia (2013) introduced a comparative study between PID and fuzzy controllers.…”

Section: Introductionmentioning

confidence: 80%

Design and Implementation of LMI-Based H2 Control for Vertical Nonlinear Coupled-Tank System

Esmaeili

Başçı

2021

International Journal of System Dynamics Applications

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This paper presents the mathematical design and implementation of a robust H_2 output feedback controller for the vertical nonlinear coupled-tank system. Considering the growth of the complicated chemical processes in industries in the last decades, the necessity for the controllers with high robustness and proficiency is demanded. Therefore, to overcome some deficiencies of classical controllers such as Proportional Integral (PI), the robust H_2 output feedback controller is proposed to control the liquid level of the coupled tank system benchmark. Because of the nonlinearity of the system and the interactions between two tanks, the behavior of the controller in terms of the performance and disturbance rejection is on the main scene. The Linear Matrix Inequalities (LMI) is used to derive the design procedure. The effectiveness of the proposed approach in the setpoint tracking is highlighted in comparison with the PI plus feedforward controller and the acceptable results are achieved.

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Section: Introductionmentioning

confidence: 80%

Design and Implementation of LMI-Based H2 Control for Vertical Nonlinear Coupled-Tank System

Esmaeili

Başçı

2021

International Journal of System Dynamics Applications

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“…The inference method used to link input to output variables was Mandani, enabling accidents at work risk analysis for each specific situation. The "non-fuzzification" was carried out by the center gravity method, as suggested by Arun and Mohan (2017).…”

Section: Methodsmentioning

confidence: 99%

Expert systems as a tool to manage accident risks in grain storage facilities

et al. 2020

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Cuidar do bem-estar e da saúde dos trabalhadores é um princípio básico de engenharia, que compreende estudos científicos e visa alcançar um resultado positivo no final dos processos. O uso de unidades de processamento e armazenamento de grãos é vantajoso, pois permite o armazenamento de produtos por longos períodos. Por outro lado, esses locais oferecem riscos aos trabalhadores. Atualmente, existe um grande número de pesquisas que utilizam sistemas especializados para auxiliar na tomada de decisões. A Lógica Fuzzy é uma das mais utilizadas, pois apresenta fácil manuseio e interpretação dos resultados. Assim, este estudo teve como objetivo utilizar a Lógica Fuzzy na Engenharia de Segurança do Trabalho, a fim de elaborar um modelo matemático que represente os níveis de risco de acidentes nas Unidades de Processamento e Armazenamento de Grãos e que possa ser utilizado como ferramenta para auxiliar na tomada de decisões. A possibilidade de acidentes; exposição ao risco; e a experiência dos trabalhadores foram consideradas variáveis de entrada. A resposta da simulação foi o risco de ocorrência de acidente ocupacional. Como conclusão, foi possível verificar que a Lógica Fuzzy foi tão eficiente quanto uma ferramenta de engenharia para representar riscos de acidentes. O cumprimento das Normas de Regulamentação (NR) e a "Experiência dos Trabalhadores"podem impactar o "Risco de acidente"nas unidades de processamento e armazenamento de grãos.

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“…In this paper, the TS fuzzy model is fused, and then the barycenter method is used to remove fuzziness to normalize the network. 24…”

Section: Normalized Layermentioning

confidence: 99%

Frequency tracking control of the WPT system based on fuzzy RBF neural network

Liu

Feng

et al. 2021

Int J of Intelligent Sys

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With the application of electrical equipment, magnetically coupled resonant (MCR) wireless power transfer (WPT) technology has become an effective means to improve equipment intelligence. The MCR‐WPT system is a loosely coupled system, and the resonant frequency may be split or detuned due to the changes of load or transferring distance, resulting in the system transfer efficiency (TE) greatly reduced. To solve the problems of limited speed and accuracy in the existing frequency tracking methods, this paper analyzes the relation between the detuning rate and the system TE, proposing an adaptive frequency tracking control method based on fuzzy radial basis function neural network control. The neural network outputs proportion–integration–differentiation parameters to adjust the inverter drive circuit, and the frequency of inverter drive circuit is adjusted nonlinearly in real time to ensure the accurate frequency tracking of the MCR‐WPT system. The simulation and experimental results show that the proposed method can enhance the tracking ability of the resonant frequency, and effectively improve the system TE.

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Modeling, stability analysis, and computational aspects of some simplest nonlinear fuzzy two-term controllers derived via center of area/gravity defuzzification

Cited by 40 publications

References 40 publications

Design and Implementation of LMI-Based H2 Control for Vertical Nonlinear Coupled-Tank System

Design and Implementation of LMI-Based H2 Control for Vertical Nonlinear Coupled-Tank System

Expert systems as a tool to manage accident risks in grain storage facilities

Frequency tracking control of the WPT system based on fuzzy RBF neural network

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