Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement

This paper presents an integrated approach combining a sequential neural network (SNN) with model predictive control (MPC) to enhance the performance of a permanent magnet synchronous motor (PMSM). We address the challenges of traditional control methods that struggle with the dynamics and nonlinear...

Full description

Saved in:
Bibliographic Details
Main Authors: Shaswat Chirantan, Bibhuti Bhusan Pati
Format: Article
Language:English
Published: AIMS Press 2024-05-01
Series:AIMS Electronics and Electrical Engineering
Subjects:
Online Access:https://www.aimspress.com/article/doi/10.3934/electreng.2024012
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832590252321013760
author Shaswat Chirantan
Bibhuti Bhusan Pati
author_facet Shaswat Chirantan
Bibhuti Bhusan Pati
author_sort Shaswat Chirantan
collection DOAJ
description This paper presents an integrated approach combining a sequential neural network (SNN) with model predictive control (MPC) to enhance the performance of a permanent magnet synchronous motor (PMSM). We address the challenges of traditional control methods that struggle with the dynamics and nonlinear nature of PMSMs, offering a solution that leverages the predictive capabilities of MPC and the adaptive learning potential of neural networks. Our SNN-MPC model is contrasted with state-of-the-art genetic algorithm (GA) and ant colony optimization (ACO) methods through a comprehensive simulation analysis. This analysis critically examines the dynamic responses, including current, torque, and speed profiles, of the PMSM under proposed hybrid control strategies. The heart of the work deals with the optimal switching states and subsequent voltage injection to the inverter fed PMSM drive by a predefined minimization principle of a current modulated objective function, where MPC constitutes an integral finite control set (IFCS) mechanism for voltage vector selection and thereby selects the optimized integral gains Kd and Kq for direct and quadrature axes, respectively, with the FCS gain Kfcs obtained from implemented intelligent techniques. Based on the control criteria, the SNN-MPC scheme was established as the preferred benchmark with optimized tuning values of Kd = 0.01, Kq = 0.006, and Kfcs = 0.13, as compared to the gain values tuned from GA and ACO. The experimental setup utilized MATLAB and a Python environment for robust and flexible simulation, ensuring an equitable basis for comparison across all models.
format Article
id doaj-art-5142d19b0153477c8f664b182a005650
institution Kabale University
issn 2578-1588
language English
publishDate 2024-05-01
publisher AIMS Press
record_format Article
series AIMS Electronics and Electrical Engineering
spelling doaj-art-5142d19b0153477c8f664b182a0056502025-01-24T01:10:27ZengAIMS PressAIMS Electronics and Electrical Engineering2578-15882024-05-018225528110.3934/electreng.2024012Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcementShaswat Chirantan0Bibhuti Bhusan Pati1Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, IndiaDepartment of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, IndiaThis paper presents an integrated approach combining a sequential neural network (SNN) with model predictive control (MPC) to enhance the performance of a permanent magnet synchronous motor (PMSM). We address the challenges of traditional control methods that struggle with the dynamics and nonlinear nature of PMSMs, offering a solution that leverages the predictive capabilities of MPC and the adaptive learning potential of neural networks. Our SNN-MPC model is contrasted with state-of-the-art genetic algorithm (GA) and ant colony optimization (ACO) methods through a comprehensive simulation analysis. This analysis critically examines the dynamic responses, including current, torque, and speed profiles, of the PMSM under proposed hybrid control strategies. The heart of the work deals with the optimal switching states and subsequent voltage injection to the inverter fed PMSM drive by a predefined minimization principle of a current modulated objective function, where MPC constitutes an integral finite control set (IFCS) mechanism for voltage vector selection and thereby selects the optimized integral gains Kd and Kq for direct and quadrature axes, respectively, with the FCS gain Kfcs obtained from implemented intelligent techniques. Based on the control criteria, the SNN-MPC scheme was established as the preferred benchmark with optimized tuning values of Kd = 0.01, Kq = 0.006, and Kfcs = 0.13, as compared to the gain values tuned from GA and ACO. The experimental setup utilized MATLAB and a Python environment for robust and flexible simulation, ensuring an equitable basis for comparison across all models.https://www.aimspress.com/article/doi/10.3934/electreng.2024012permanent magnet synchronous motormodel predictive controlgenetic algorithmant colony optimizationsequential neural networkvoltage source inverterfinite control setintegral finite control set
spellingShingle Shaswat Chirantan
Bibhuti Bhusan Pati
Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement
AIMS Electronics and Electrical Engineering
permanent magnet synchronous motor
model predictive control
genetic algorithm
ant colony optimization
sequential neural network
voltage source inverter
finite control set
integral finite control set
title Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement
title_full Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement
title_fullStr Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement
title_full_unstemmed Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement
title_short Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement
title_sort integration of predictive and computational intelligent techniques a hybrid optimization mechanism for pmsm dynamics reinforcement
topic permanent magnet synchronous motor
model predictive control
genetic algorithm
ant colony optimization
sequential neural network
voltage source inverter
finite control set
integral finite control set
url https://www.aimspress.com/article/doi/10.3934/electreng.2024012
work_keys_str_mv AT shaswatchirantan integrationofpredictiveandcomputationalintelligenttechniquesahybridoptimizationmechanismforpmsmdynamicsreinforcement
AT bibhutibhusanpati integrationofpredictiveandcomputationalintelligenttechniquesahybridoptimizationmechanismforpmsmdynamicsreinforcement