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

Shaswat Chirantan; Bibhuti Bhusan Pati; Shaswat Chirantan; Bibhuti Bhusan Pati

doi:10.3934/electreng.2024012

AIMS Electronics and Electrical Engineering

2024, Volume 8, Issue 2: 255-281. doi: 10.3934/electreng.2024012

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Research article

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

Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, India

Academic Editor: Fanny Spagnolo

Received: 28 March 2024 Revised: 07 May 2024 Accepted: 14 May 2024 Published: 22 May 2024

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 K_d and K_q for direct and quadrature axes, respectively, with the FCS gain K_fcs 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 K_d = 0.01, K_q = 0.006, and K_fcs = 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.
- 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
Citation: Shaswat Chirantan, Bibhuti Bhusan Pati. Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement[J]. AIMS Electronics and Electrical Engineering, 2024, 8(2): 255-281. doi: 10.3934/electreng.2024012

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Abstract

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 K_d and K_q for direct and quadrature axes, respectively, with the FCS gain K_fcs 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 K_d = 0.01, K_q = 0.006, and K_fcs = 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.

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