Torque Error Based Auto-tuning of Weighting Factor in Model Predictive Torque Control of Induction Motor Drive

Shahid, Muhammad Bilal; Jin, Weidong; Abbasi, Muhammad Abbas; Husain, Abdul Rashid; Hassan, Mannan

doi:10.1007/s42835-022-01250-9

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…This task requires finding the optimal combination of weights to assign to various terms in the cost function, which is essential for achieving the desired control performance. This issue has been addressed in previous studies [ 29 , 30 ].…”

Section: System Controlmentioning

confidence: 88%

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters

Gada,

Fekik,

Mahdal

et al. 2023

Sensors

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The Single-Stage Grid-Connected Solar Photovoltaic (SSGC-SPV) topology has recently gained significant attention, as it offers promising advantages in terms of reducing overall losses and installation costs. We provide a comprehensive overview of the system components, which include the photovoltaic generator, the inverter, the Incremental Conductance Maximum Power Point Tracking (IC-MPPT) algorithm, and the PI regulator for DC bus voltage control. Moreover, this study presents detailed system configurations and control schemes for two types of inverters: 2L−3PVSI and 3L−3PNPC. In order to perform a comparative study between the two structures, we subjected them to the same irradiation profile using the same grid configuration. The Photovoltaic Array (PVA) irradiance is increased instantaneously, in 0.2 s, from 400 W/m2 to 800 W/m2, is kept at 800 W/m2 for 0.2 s, is then gradually decreased from 800 W/m2 to 200 W/m2 in 0.2 s, is then kept at 200 W/m2 for 0.2 s, and is then finally increased to 1000 W/m2 for 0.2 s. We explain the operational principles of these inverters and describe the various switching states involved in generating output voltages. To achieve effective control, we adopt the Finite Set–Model Predictive Control (FS-MPC) algorithm, due to the benefits of excellent dynamic responsiveness and precise current tracking abilities. This algorithm aims to minimise the cost function, while taking into account the dynamic behaviour of both the PV system and the inverter, including any associated delays. To evaluate the performance of the FS-MPC controller, we compare its application in the three-level inverter configuration with the two-level inverter setup. The DC bus voltage is maintained at 615 V using the PI controller. The objective is to achieve a Total Harmonic Distortion (THD) below 5%, with reference to the IEEE standards. The 2L−3PVSI inverter is above the threshold at an irradiance of 200 W/m2. The 3L−3PNPC inverter offers a great THD percentage, meaning improved quality of the power returned to the grid.

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Section: System Controlmentioning

confidence: 88%

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters

Gada,

Fekik,

Mahdal

et al. 2023

Sensors

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“…Cao et al [26] produced a refection on the BLDCM braking case and proposed a smooth torque control strategy for the BLDCM that corresponds to the direct torque control performance in diferent speed ranges according to diferent modulation methods to achieve controllability of the braking torque and also reduce the phase change torque. Te literature [27] uses numerical analysis ideas in the optimization of MPTC systems to linearize the torque function and current constraints, eliminating gain tuning according to the characteristics of the control strategy. Choi et al focuses attention on the complex weighting factors in MPTC, and proposes an online adjustment of the weighting factors in the literature [28] to keep the error within a certain range.…”

Section: A Brief Summary Of Recent Research Reportsmentioning

confidence: 99%

Optimized Model Torque Prediction Control Strategy for BLDCM Torque Error and Speed Error Reduction System

Yuan,

Liu,

Chen

et al. 2023

Journal of Electrical and Computer Engineering

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This paper presents an improved whale optimization algorithm (IWOA) for optimizing the model predictive torque control (MPTC) of brushless DC motor (BLDCM) to further reduce the problems of strong torque pulsation and high ripple caused by the special structure of BLDCM. IWOA adds a randomized convergence factor strategy to the original algorithm, enabling the parameter weights to be adjusted in time. The relative error between the training set and the predicted values is reduced, and a suitable interval is selected for the target. The proposed method takes into account the switching frequency loss factor in the MPTC system of BLDCM, discarding the traditional trial-and-error method and choosing to control the parameter adjustment by the degree of deviation. The IWOA is compared with the popular whale optimization algorithm (WOA), dragonfly algorithm (DA), ant colony optimization (ACO) algorithm, and grey wolf optimization (GWO) algorithm on the MATLAB SIMULINK platform to verify the effectiveness of the method in dealing with improved chain tracking, reduced torque pulsation, and reduced speed error. The simulation results show that IWOA performs well, with an efficiency of 94.32%.

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“…In this paper, we mainly apply FCS-MPTC with the objective of controlling torque and stator magnetic chain.In practice, the weight factor adjustment in FCS-MPTC relies too much on exhaustive and empirical methods, which is very bulky and unsatisfactory performance results when dealing with over control objectives and influence weight factors. The literature [6] incorporates the idea of numerical analysis in dealing with the weighting factor problem by linearizing the target and eliminating the gain tuning according to the characteristic function; the literature [7] uses the online adjustment of the weighting factor method, which makes the weighting factor in MPTC simple; the literature [8] uses fuzzy control to adjust the weighting factor, but it is too cumbersome and difficult to implement in setting fuzzy rules, which is not conducive to promote the use.…”

Section: Introductionmentioning

confidence: 99%

Model predictive torque control of brushless DC motor based on improved whale optimization algorithm

Yuan,

liu,

et al. 2023

Third International Conference on Control and Intelligent Robotics (ICCIR 2023)

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Torque Error Based Auto-tuning of Weighting Factor in Model Predictive Torque Control of Induction Motor Drive

Cited by 6 publications

References 29 publications

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters

Optimized Model Torque Prediction Control Strategy for BLDCM Torque Error and Speed Error Reduction System

Model predictive torque control of brushless DC motor based on improved whale optimization algorithm

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