Real-time Implementation of a Takagi-Sugeno Fuzzy Controller for a DC-DC Boost Converter

Daouadi, Rabah; Ounass, Djamel; Soufi, Youcef; Metatla, Abdrezak; Guiza, Dhaouadi

doi:10.46904/eea.20.68.4.1108012

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Therefore, the development of a speed controller that eliminates external disturbances and sustains road uncertainty with reduced control action has become a key issue to be addressed. [5][6][7][8][9][10] DC-DC converter is an important component for an EV. Under normal conditions, the vehicle maintains its stability.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to changing road dimensions, load parameters, and external disturbances, the control system is nonlinear, timedependent, and unpredictable. Therefore, the development of a speed controller that eliminates external disturbances and sustains road uncertainty with reduced control action has become a key issue to be addressed 5–10 . DC‐DC converter is an important component for an EV.…”

Section: Introductionmentioning

confidence: 99%

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A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

Priya,

Balakumaran

2023

Asia-Pacific J Chem Eng

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Nowadays, the use of fuel cells in various applications has become very widespread. Some of their most important applications are in electric vehicles (EVs) and local off‐grid power systems. An EV works as an electric motor that burns a mixture of fuel and gases to generate electricity. One of the challenges with fuel cells is the slow dynamic response to load power changes. In the past few decades, the modeling and control of DC‐DC converters have undergone extensive research and development. However, the error, settling time, and peak overshoot performance are not reduced by the robust nonlinear controller and current mode controller. To overcome these problems, quantile regressive extreme seeking cat swarm optimized Mamdani fuzzy PI controller (QRESCSO‐MFPIC) approach is developed. The goal of the QRESCSO‐MFPIC approach is to reduce the integral time absolute error (ITAE) for tuning the fuzzy PI controller. Initializing the fuzzy PI controller parameter is an input in the QRESCSO‐MFPIC approach. For every parameter value, the fitness function is determined and quantile regression analysis is carried out. The efficiency of the QRESCSO‐MFPIC approach is measured by evaluating the settling time and peak overshoot. Tuning of the PI controller is carried out with cat swarm optimization (CSO) and particle swarm optimization (PSO) based on the objective function of ITAE. The result analysis shows that the QRESCSO‐MFPIC approach improves the efficiency of an optimized Fuzzy PI controller compared to the existing methods. Peak overshoot reduction and settling time are improved through the QRESCSO‐MFPIC approach than the proportional integral particle swarm optimization controller (PI‐PSO).

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