Adaptive Fuzzy-PI for Induction Motor Speed Control

Maghfiroh, Hari; Saputro, Joko Slamet; Fahmizal, Fahmizal; Baballe, Muhammad Ahmad

doi:10.59247/jfsc.v1i1.24

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The shaft speed of the IG linked to the prime mover is noted for the proposed control, and the data is passed to a comparator circuit. A fuzzy controlled proportional-integral controller (PI) similar to fuzzy-PID [17]- [18] receives the set error and the change in error value. After comparison of the shaft speed the reference speed, the signal is sent to fuzzify and further modify the PI controller gains.…”

Section: Proposed Control Schemementioning

confidence: 99%

Analysis of a Self-excited Induction Generator with Fuzzy PI Controller for Supporting Domestic Loads in a Microgrid

Chatterjee

2023

JFSC

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This study provides a technical analysis of integrating a microgrid structure for power generation with a wind energy conversion system. This study's primary objective is to identify solutions to issues that arise when isolated induction generator is utilized in a microgrid. A closed loop electronic load connector is used to control the loads of the induction generator-based system. The load controller is adjusted using an adaptive fuzzy based proportional-integral controller for better control. Additionally, by employing a straightforward variable capacitor design to inject reactive power during voltage dips, it provides a way to sustain the voltage profile. The suggested approach and its control are investigated in MATLAB/Simulink and verified with the aid of a lab experimental setup. The outcomes clearly demonstrate the practicality of the suggested plan to be implemented in grid-isolated places.

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Section: Proposed Control Schemementioning

confidence: 99%

Analysis of a Self-excited Induction Generator with Fuzzy PI Controller for Supporting Domestic Loads in a Microgrid

Chatterjee

2023

JFSC

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“…However, this method is sensitive to the parameter's changes. On the other hand, DTC provides good performance and consideration for parameter variation [14]. The FOC approach is further broken down into Direct-FOC and Indirect-FOC (IFOC).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Linear Quadratic Gaussian Speed Control of Induction Motor Using Fuzzy Logic

Maghfiroh,

Ma’arif,

Adriyanto

et al. 2023

JESA

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An induction motor's speed can be managed in a variety of ways using a Variable Frequency Drive (VFD). In this study, the speed control of an induction motor will be controlled by applying Indirect Field Oriented Control (IFOC) combined with Linear Quadratic Gaussian (LQG). Conventional LQG control is a linear controller; therefore, if the system's dynamic is high and over the linear boundary, the LQG performance will not be optimal. Therefore, Adaptive LQG (ALQG) is proposed. Fuzzy logic is used as an adaptive algorithm with low complexity and ease of implementation. The significance of this study lies in its endeavor to tackle the challenges associated with nonlinearities and high dynamics in induction motor control. The average performance of speed variation and load variation tests proves that ALQG is superior in terms of settling time and undershooting than PID and LQG. PID has the highest overshoot with the smallest Integral Absolute Error (IAE). In comparison, ALQG is superior to conventional LQG in terms of IAE with 3.59% lower.

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Advanced hybrid control for induction motor combining ANN-PID speed controller with neural predictive current regulator based on particle swarm optimization

Rafia,

Ouadi,

Giri

et al. 2024

Transactions of the Institute of Measurement and Control

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This paper presents a hybrid control strategy for an induction motor (IM) organized into two nested loops. In the outer loop, an adaptive artificial neural network-based proportional-integral-derivative (ANN-PID) controller is used for speed control. The ANN-PID is divided into two stages: the first stage includes an adaptive ANN speed estimator, and the second stage includes an adaptation algorithm that fine-tunes both the weights and biases of the ANN estimator and the parameters of the PID controller. In the internal loop, a predictive current controller is used to control IM currents using neural networks. Specifically, the neural predictive controller (NPC) approaches the control problem by presenting it as an optimization problem using a neural predictor for IM currents. The considered objective function includes two elements: the current tracking error and the electromagnetic torque ripple. This function is computed over a given time horizon informed by predictions of IM currents. The particle swarm optimization (PSO) algorithm is applied to determine the optimal solution for this optimization task. The effectiveness of the hybrid control scheme is demonstrated by simulation results obtained using Matlab/Simulink, highlighting its superiority over conventional control methods. The proposed hybrid control is characterized by a reduction in torque ripple and overshoot, while also showing robustness to variations in load, rotor resistance, and rotor inductance. Comparisons with an ANN controller and a fuzzy PI controller further demonstrate the superior performance of the hybrid controller in handling nonlinear dynamics and maintaining control accuracy under various operating scenarios.

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Adaptive Fuzzy-PI for Induction Motor Speed Control

Cited by 3 publications

References 11 publications

Analysis of a Self-excited Induction Generator with Fuzzy PI Controller for Supporting Domestic Loads in a Microgrid

Analysis of a Self-excited Induction Generator with Fuzzy PI Controller for Supporting Domestic Loads in a Microgrid

Adaptive Linear Quadratic Gaussian Speed Control of Induction Motor Using Fuzzy Logic

Advanced hybrid control for induction motor combining ANN-PID speed controller with neural predictive current regulator based on particle swarm optimization

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