Implementation of Fractional Pi Controller for Optimal Speed Control of Induction Motor Fed With Quasi Z-Source Converter

Singh, Deepak; Malar, R. Suja Mani

doi:10.1016/j.micpro.2020.103323

Cited by 3 publications

(6 citation statements)

References 17 publications

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“…The mathematical description of HEVs is categorized into two main parts [10][11][12][13][14][15][16][17][18][19]. The first is related to mechanical components in the vehicle such as the engine and the dynamics of the vehicle body.…”

Section: System Modelingmentioning

confidence: 99%

“…In the engine, the crankshaft is modeled with the equilibrium equation of motion as in Equation ( 2) [10][11][12][13][14][15][16][17][18][19].…”

Section: Purpose Of Engine Managementmentioning

confidence: 99%

“…Moreover, the actual torque is estimated based on the PI observer in [11]. Particle swarm optimization is used to find the optimal parameters of a fractional order PID controller to track the position of the DC motor clutch in [12]. The FO-PI was introduced by the authors of [13] to achieve better control performance at lower speeds with high torque per ampere as a motor output, where it was experimentally proven to be a better indirect field-oriented control method for induction motors.…”

Section: Introductionmentioning

confidence: 99%

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Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle

et al. 2023

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The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation.

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Section: System Modelingmentioning

confidence: 99%

“…In the engine, the crankshaft is modeled with the equilibrium equation of motion as in Equation ( 2) [10][11][12][13][14][15][16][17][18][19].…”

Section: Purpose Of Engine Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle

et al. 2023

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“…In 2020, D Bensiker Raja Singh and, R Suja Mani Malar [1], focused by means of the well-organized control model. The contribution of the developed model was to discover the effectual model to maintain a dynamic and steady-state analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

“…DGs are decentralized power generation sources that are compressed, competent, and mainly based upon renewable energy like PV system, FC, and wind energy, so on. Hence, renewable energy idea-based MG and its management has been developed as important fields of research in the energy segment [1].…”

Section: Introductionmentioning

confidence: 99%

Improved Butterfly Optimization Algorithm: PI Controller for 7-Level Inverter

Sangtani¹

2021

JCMPS

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In environmental parameters, the Photovoltaic (PV) system generates electricity which varies with deviations as well as at the Maximum Power Point (MPP) the PV network will operate. In addition, to improve the effectuality and also the Renewable Energy Sources (RES) performance, the energy storage devices can act as a possible solution. Moreover, this work tries to set up a control model using an optimization algorithm aided PI controller for the seven-level inverter. On the basis of this, the Proportional Integral (PI) controller increases are attuned dynamically using the Improved Butterfly Optimization Algorithm (IBOA). The increase must be maintained so that the error between the fault signal and reference signal must be the least. Therefore, using the developed optimized PI controller, superior dynamic performance can be attained. At last, the developed model performance is evaluated with the conventional models regarding various metrics, and the proposed method shown its dominance over the conventional models.

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Hybrid Backstepping-Super Twisting Algorithm for Robust Speed Control of a Three-Phase Induction Motor

2023

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This paper proposes a Hybrid Backstepping Super Twisting Algorithm for robust speed control of a three-phase Induction Motor in the presence of load torque uncertainties. First of all, a three-phase squirrel cage Induction Motor is modeled in MATLAB/Simulink. This is then followed by the design of different non-linear controllers, such as sliding mode control (SMC), super twisting SMC, and backstepping control. Furthermore, a novel controller is designed by the synergy of two methods, such as backstepping and super twisting SMC (Back-STC), to obtain the benefits of both techniques and, thereby, improve robustness. The sigmoid function is used with an exact differentiator to minimize the high-speed discontinuities present in the input channel. The efficacy of this novel design and its performance were evidenced in comparison with other methods, carried out by simulations in MATLAB/Simulink. Regression parameters, such as ISE (Integral Square error), IAE (Integral Absolute error) and ITAE (Integral Time Absolute error), were calculated in three different modes of operation: SSM (Start-Stop Mode), NOM (Normal Operation Mode) and DRM (Disturbance Rejection Mode). In the end, the numerical values of the regression parameters were quantitatively analyzed to draw conclusions regarding the tracking performance and robustness of the implemented non-linear control techniques.

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Implementation of Fractional Pi Controller for Optimal Speed Control of Induction Motor Fed With Quasi Z-Source Converter

Cited by 3 publications

References 17 publications

Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle

Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle

Improved Butterfly Optimization Algorithm: PI Controller for 7-Level Inverter

Hybrid Backstepping-Super Twisting Algorithm for Robust Speed Control of a Three-Phase Induction Motor

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