Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller

Salahuddin, Humayun; Imdad, Kashif; Chaudhry, Muhammad Umar; Nazarenko, Dmitry; Bolshev, Vadim; Yasir, Muhammad

doi:10.3390/app12094647

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…When the derivative time is too long, fluctuations occur in this mode and the closed loop becomes unstable. Then, in real-world applications, the PID parameters should be carefully tuned [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A New Type-3 Fuzzy PID for Energy Management in Microgrids

Fan

Mohammadzadeh

Kausar

et al. 2022

Advances in Mathematical Physics

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More recently, type-3 (T3) fuzzy logic systems (FLSs) with better learning ability and uncertainty modeling have been presented. On other hand, the proportional-integral-derivative (PID) is commonly employed in most industrial control systems, because of its simplicity and efficiency. The measurement errors, nonlinearities, and uncertainties degrade the performance of conventional PIDs. In this study, for the first time, a new T3-FLS-based PID scheme with deep learning approach is introduced. In addition to rules, the parameters of fuzzy sets are also tuned such that a fast regulation efficiency is obtained. Unlike the most conventional approaches, the suggested tuning approach is done in an online scheme. Also, a nonsingleton fuzzification is suggested to reduce the effect of sensor errors. The proposed scheme is examined on a case-study microgrid (MG), and its good frequency stabilization performance is demonstrated in various hard conditions such as variable load, unknown dynamics, and variation in renewable energy (RE) sources.

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Section: Introductionmentioning

confidence: 99%

A New Type-3 Fuzzy PID for Energy Management in Microgrids

Fan

Mohammadzadeh

Kausar

et al. 2022

Advances in Mathematical Physics

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The use of a Mamdani-type fuzzy model for assessing the performance of a boom stabilization systems in a field sprayer

Kaliniewicz,

Szczyglak,

Lipiński

et al. 2023

Sci Rep

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Fuzzy logic models are increasingly used to control simple and complex devices, as well as entire operating systems. In this study, a fuzzy logic model was applied to assess the performance a boom stabilization system in a field sprayer. The model was tested on a field sprayer with a trapezoid system for stabilizing the sprayer boom with a length of 21 m. Measuring cables for registering the displacement of the boom's terminal segments (right and left) in the vertical and horizontal plane were installed on the sprayer. The field sprayer was connected to a tractor. The model was based on two linguistic variables: "absolute displacement of the boom's terminal segments" and "boom stability index". It was assumed that the sprayer boom was stable when the displacement of the boom's terminal segments did not exceed 0.25% of boom length. The study demonstrated that the proposed model can be reliably used to assess boom stability in real time (during field operations). The time required to achieve boom stability was more than 2.5 times shorter in the vertical than in the horizontal plane, which can be attributed mainly to the structure of the stabilization system. The proposed model is universal, and it can be applied to evaluate other boom stabilization systems in field sprayers.

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Optimizing Motor Performance with Improved Fractional Order Darwinian Particle Swarm Optimization and Fuzzy Logic Controllers: A Comparative Study in Torque Control

Ajmal Mahasin,

Sanjay,

Rathinavel

et al. 2024

E3S Web of Conf.

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Utilizing fuzzy logic controllers (FLCs) and the improved fractional ordered Darwinian particle swarm optimization method (IFODPSO), this paper demonstrates a way to boost motor performance. The system is able to generate torque instantly and respond quickly because it uses direct torque control (DTC) approaches that are regulated by IFODPSO. Motor performance improvement with IFODPSO and fuzzy logic controllers is the motive of this study. The regulation of torque in motor applications is the particular issue that is being tackled. The research compares the IFODPSO-FLC method's performance with that of typical field-oriented control (FOC) method and DTC method. In contrasted with more conventional FOC and DTC methods, the results obtained by the IFODPSO-FLC methodology show promise for torque control, highlighting the significance of the findings. To further enhance system efficiency at low speeds, the suggested PI-fuzzy opposition estimation accounts for fluctuations in stator resistance. An innovative and effective strategy is the integration of fractional-order FLCs with Darwinian particle swarm optimization (DPSO). The outcomes are assessed with the use of MATLAB-Simulink and the performance that is derived from them shows promise for effective motor control applications.

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Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller

Cited by 6 publications

References 32 publications

A New Type-3 Fuzzy PID for Energy Management in Microgrids

A New Type-3 Fuzzy PID for Energy Management in Microgrids

The use of a Mamdani-type fuzzy model for assessing the performance of a boom stabilization systems in a field sprayer

Optimizing Motor Performance with Improved Fractional Order Darwinian Particle Swarm Optimization and Fuzzy Logic Controllers: A Comparative Study in Torque Control

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