Search Results (173)

Switched reluctance motors (SRMs) are attractive for fault-tolerant drives because their rare-earth-free rotor and intrinsic phase isolation support continued operation after a converter fault. Realising this requires a post-fault control policy that preserves both torque tracking and per-phase thermal balance, with the latter being a safety-relevant design consideration motivated by—though not herein verified against—ISO 26262. This paper proposes and evaluates, by simulation, a three-layer fault-tolerant finite-control-set model predictive control (FCS-MPC) architecture for a four-phase 8/6 SRM under a single open-phase converter fault. The layers are (i) a vector-set reconfiguration from the eight healthy, active vectors to the twenty-six admissible post-fault vectors, which restores controllability of the reduced converter; (ii) soft commutation expressed as a position-dependent penalty inside the MPC cost; and (iii) asymmetry-aware balancing that evens out the accumulated thermal load across the three healthy phases. We additionally analyse an activated-on-demand max-penalty thermal limiter and show, both analytically and in simulation, that it shares its optimiser with the variance-based balancing term and therefore confers no measurable benefit over it; it is consequently retained only as an optional on-demand limiter rather than a separate layer. The architecture is benchmarked against a fault-blind baseline, a rule-based hard fault-tolerant reference (Hard-FT), and intermediate configurations through a deterministic ablation across three critical operating points, complemented by a robustness assessment under measurement noise and parameter mismatch. A six-criteria fault-tolerance scorecard is reported as a methodological observation on the transferability of healthy-mode SRM specifications to post-fault operation. Full article

The paper presents a simulation and experimental study of a three-phase 12/8 Switched Reluctance Motor (SRM) operating in motoring mode. The operation of the asymmetric bridge converter is analyzed, and the mathematical equations describing the phase-current change under various commutation states in soft-switching mode (modulation of the upper transistors) are derived. An analytical model is used to examine the energy exchange between the battery, the power switches, and the phase inductance. For the purposes of the study, a simulation model was developed in the MATLAB/Simulink R2025b environment, including models of the battery, the power converter, and the SRM. Simulation studies were conducted under various load conditions and phase current values, yielding time-domain waveforms of the phase currents and voltages, as well as the electromagnetic torque. Experimental waveforms of the phase current and voltage, measured under conditions corresponding to those in the simulation studies, are presented. A comparative analysis was performed between the simulation and experimental results, with tabular and graphical dependencies presented, and the relative error between them determined. The results obtained show good agreement between the simulation model and the actual system, which confirms the applicability of the developed approach for the analysis and optimization of SRM drives. Full article

Theswitched reluctance motor (SRM) suffers from torque ripple and speed fluctuations because of its doubly salient structure, magnetic saturation, and discrete commutation. To improve commutation performance and disturbance rejection, this paper proposes a progressive torque ripple suppression strategy. First, a multimodal magnetic co-energy model is developed to describe position-dependent saturation and generate the reference current through model inversion. Then, envelope extraction and frequency identification reveal the commutation-related periodic torque-error characteristic. Based on this feature, an angle-domain binned compensation method combining cycle averaging and linear interpolation is proposed to correct the reference current. A score-based finite-set PI hysteresis current controller is further designed to optimize magnetizing, freewheeling, and demagnetizing states, while a linear active disturbance rejection control (LADRC) speed loop improves load-disturbance rejection. Ablation studies verify the synergistic effect between angle-domain compensation and finite-set current execution. Robustness tests confirm low sensitivity to parameter variations, and theoretical analysis proves ultimate boundedness. Simulation results show that torque ripple is reduced to 2.00%, 2.03%, and 2.18% at 250, 500, and 1000 r/min, respectively. Under load-disturbance conditions, speed fluctuation is reduced by 59.92% and 57.20%, and all normalized parameter sensitivities remain below 0.35. Full article

It is essential to experimentally evaluate a Switched Reluctance Motor’s (SRM) flux-linkage characteristics to verify that its magnetic behavior aligns with design targets. This paper presents the development of a novel, fully automated custom experimental test bed and a control model capable of characterizing the static self- and mutual flux linkages of a switched reluctance motor. The proposed setup is programmed with MATLAB/Simulink for automatic characterization across various rotor positions and excitation currents, which has not been previously addressed in the literature. The automated measurement algorithm is implemented and validated on a 70 kW, 18/12 propulsion SRM prototype. Flux-linkage data is obtained across a full 360° mechanical rotation, with self-flux linkages measured up to 210 A and mutual flux linkages up to 130 A. Experimental results indicate a maximum 6% deviation from the finite element analysis (FEA) results for mutual flux linkage and below 5% for self-flux linkage. The developed flux-linkage characterization approach demonstrates good accuracy and repeatability, enabling the construction of reliable flux–current–position datasets essential for SRM modeling and validation. Full article

Synchronous reluctance motors in quadruped robot joint drives are prone to active-current peaks, abrupt rate variations, and switching-neighborhood error concentration under foot–ground impacts and obstacle-induced load steps, leading to prediction lag and peak underestimation. To address these issues, this paper proposes a dynamic-response-enhanced multi-condition active-current prediction method based on TPE-VMD-BiLSTM-TRC. First, the original sequence is segmented according to operating-condition boundaries, and prediction samples are constructed within each segment to reduce cross-condition information leakage and distribution inconsistency. Second, variational mode decomposition is performed on each segmented sequence to separate multi-scale fluctuation components, and BiLSTM is used for mode-wise one-step prediction. Third, TPE jointly optimizes the VMD decomposition parameters and BiLSTM hyperparameters to improve parameter matching under different operating conditions. Furthermore, a switching-aware composite loss and a switch-gated residual correction branch are introduced to enhance dynamic tracking and compensate for structural bias in switching neighborhoods. Experiments on variable-frequency drive data show that, compared with TPE-VMD-BiLSTM, the proposed method reduces the overall RMSE, switching-neighborhood RMSE, and MAPE by approximately 29.8%, 38.8%, and 7.4%, respectively. On the additional held-out operating-condition segment, the overall RMSE is reduced by 28.8%, indicating stable prediction performance across different segments. Full article

In this study, a novel direct torque control (DTC) strategy is proposed to mitigate the torque ripple issue inherent in switched reluctance motors (SRMs), which is caused by the double salient pole configuration and the pulse power supply mode. The strategy is based on the prediction and optimization of a long-time-domain model. Central to this method is the development of a multi-step predictive optimization framework. By incorporating hysteresis control, the conventional approach of minimizing instantaneous error in predictive control is shifted towards minimizing tracking error over an extended time frame. A dual-objective evaluation function is also introduced, which simultaneously optimizes both torque smoothness and switching frequency, ensuring their collaborative enhancement. To validate the proposed method, a 6/4-pole SRM simulation model was implemented using MATLAB/Simulink 2024B, and comparisons were made with traditional methods. The results demonstrate that this strategy significantly reduces torque pulsation and lowers the system’s switching frequency, even under varying operational conditions such as different rotational speeds and sudden load variations. Consequently, this approach not only guarantees improved dynamic performance but also enhances the motor’s efficiency and stability. Full article

Advances in semiconductor technologies, particularly in power transistors and switching diodes, have enabled higher switching frequencies and converter efficiency, renewing interest in Switched Reluctance Motors (SRMs) for electric vehicles. This work presents a data-driven approach utilizing a Long Short-Term Memory (LSTM) network capable of effectively managing temporal dependencies for estimating rotor position without sensors in SRMs. The motor investigated was custom-designed, subsequently manufactured as a prototype. The LSTM was trained and validated with experimental data collected at various speeds and load conditions. The outcomes demonstrate the model’s strong performance, with a mean squared error (MSE) of ${1.77}^{{°}^2}$, a mean absolute error (MAE) of 1.09°, and 97.35% accuracy. Compared to typical estimation methods such as back-electromotive force (EMF)-based techniques, fuzzy logic, model predictive control, feed-forward neural networks (FFNNs), and back-propagation neural networks (BPNNs), the LSTM stands out as one of the most effective and widely used models. Previous neural networks (NN)-based studies typically report ±5° accuracy, whereas LSTM keeps the error about 1° in this study. This strategy eliminates position sensors, reduces cost and complexity, and enables reliable real-time SRM control. Results indicate that the method has significant potential for electric motor drives, particularly for SRMs. Full article

To reduce the computational burden of 3D finite element analysis for hybrid-flux high-speed switched reluctance motors (HFHSRMs), a quasi-3D parameterized equivalent magnetic network (EMN) is proposed. A parameterized radial–circumferential cross-grid is used to discretize the stator, air-gap, and rotor regions, and axial coupling branches are introduced to represent key 3D flux paths. Rotor rotation and rotor dislocation are implemented through a circumferential node-shift mapping, thereby avoiding topology reconstruction at different rotor positions. Core nonlinearity is incorporated using a piecewise fit of measured B–H data, and sparse-matrix assembly is adopted to improve solution efficiency. Based on the proposed EMN, key electromagnetic quantities are evaluated, including air-gap flux density, static characteristics, and dynamic characteristics. The results are validated against 3D finite element method (FEM) and prototype experiments. In the prototype experiments, the EMN prediction errors of key quantities are within 6%. In addition, computational efficiency is significantly improved compared with the 3D FEM, enabling rapid parameter iteration and early-stage design evaluation for HFHSRMs. Full article

This study presents a novel cooperative control strategy designed to mitigate torque ripple and enhance the disturbance rejection capability of switched reluctance motors (SRMs). The proposed approach integrates model predictive control (MPC) with direct instantaneous torque control (DITC), leveraging the torque sharing function (TSF) to generate phase-specific reference torque profiles. MPC employs rolling optimization to compute the optimal duty cycle in real time, achieving low torque ripple and consistent switching frequency during steady-state operation. To overcome the inherent delay in MPC’s dynamic response, DITC is incorporated as a fast-acting compensation loop that activates immediately upon detecting abrupt variations in speed or load, thereby delivering rapid torque adjustment and reinforcing system resilience. For validation, an 8/6-pole SRM control model was developed using Ansys/Maxwell and MATLAB/Simulink, and subjected to multi-scenario simulations. The results reveal that, compared to conventional MPC, the proposed method reduces steady-state torque ripple by 19.4% and shortens dynamic recovery time by 40%, demonstrating superior torque smoothness and improved robustness against external disturbances. Full article

The aim of this research is to investigate and reduce the torque ripple in Switched Reluctance Motor (SRM) drives, which is one of the major barriers to their acceptance for electric vehicle propulsion applications despite the advantages of robustness, efficiency, and wide operating range. High torque ripple not only deteriorates drive smoothness but also contributes to noise and vibration, demanding an advanced control strategy beyond traditional current-shaping and switching-based approaches. In this context, this work proposes a DDPG (Deep Deterministic Policy Gradient) Actor–Critic Neural Network-based reinforcement learning control framework that learns the optimal firing angle offsets dynamically to ensure less ripple electromagnetic torque under varying speeds and load conditions. The developed strategy has been designed and trained in MATLAB Simulink R2024b and then deployed in real time using an FPGA-based digital controller for validation on hardware. Comparative analysis with TSF (Torque Sharing Function) and DITC (Direct Instantaneous Torque Control) demonstrates that the reinforcement learning approach gives a much smoother torque response with better dynamic behavior over the operating range analyzed. Full article

This article focuses on power supply control issues in high-speed switched reluctance motors (SRMs). The primary scientific objective of this study was to determine whether and to what extent, the controller itself imposes limitations on SRM drive operation at very high rotational speeds, and to identify the maximum achievable speed range resulting from these limitations. Unlike most existing studies, which focus mainly on motor or power electronics constraints, this work explicitly analyses the dynamic limitations introduced by the control system architecture. An analysis of the essential controller functionalities required for implementing the SRM drive control algorithm with a C-dump converter was performed. The control system, composed of specialised hardware modules operating concurrently, was implemented in an field-programmable gate array (FPGA) device. Simulation and experimental investigations were conducted to evaluate signal propagation delays within the FPGA and their impact on the motor control process. Key functional modules contributing to the maximum signal propagation delays were identified, enabling a direct determination of the maximum motor speed at which correct power supply operation can be ensured. Furthermore, delays introduced by the power electronic components were characterized for the developed test controller, allowing a comprehensive assessment of both control and hardware-induced speed limitations. The research concluded that the FPGA-based controller introduces no significant limitations to the drive’s maximum speed. The maximum speed is limited by the mechanical constraints of the rotor and the inertia of the phase windings. Furthermore, expanding the controller with additional functionality does not significantly slow down the control algorithm’s execution. Full article

In response to the “carbon peak and carbon neutrality” strategy, industrial energy conservation has become increasingly important. Interior Permanent Magnet Synchronous Motors (IPMSMs) exhibit significant potential for efficient flux-weakening control due to their asymmetric rotor reluctance. However, conventional control strategies often cause instability during transitions across speed zones. This paper proposes a novel adaptive fuzzy-based smooth transition strategy to address this issue. First, a composite control framework integrating Maximum Torque per Ampere (MTPA) and leading-angle control is established to enhance flux-weakening capability. Then, within this framework, adaptive fuzzy controllers are designed for different weakening zones, incorporating a Lyapunov-based parameter adaptation mechanism for real-time compensation. Simulation results demonstrate that the proposed strategy achieves smooth switching across the entire speed range of IPMSMs. Quantitatively, it reduces speed overshoot by 5–15%, suppresses torque ripple by over 10%, and virtually eliminates switching current pikes compared to conventional methods, thereby significantly improving system dynamic performance and operational reliability. Full article

The switched reluctance motors (SRMs) are an attractive solution for electric vehicles (EVs) and hybrid electric vehicles (HEVs). However, the main drawbacks of SRMs are their highly nonlinear magnetic characteristics, complicated control algorithms, and the inherent torque ripples. This paper presents a simple structure average torque control (ATC) technique with a better ability to reduce torque ripples. Based on the detailed analysis of an inductance profile, this paper introduces a novel current compensation mechanism (CCM) that has the ability to profile the phase current and, hence, reduce the torque ripple. The proposed CCM is meant for the minimum inductance zone (MIZ) to profile the current of the ongoing phase. Over the MIZ, the inductance is independent of the phase current that helps to simplify the deduced mathematical formulations and provides a simple structure ATC with a lower computational burden, making it a feasible solution for real-time implementations and future developments. A series of experimental results are achieved to show the feasibility and effectiveness of the proposed ATC technique. The results show the superior performance of the proposed ATC, providing better torque profiles and reducing the torque ripples with an average value of 30% compared to conventional ATC. Full article

Switched reluctance motors (SRMs) are promising for applications such as air compressors due to their robust structure and fault tolerance, but suffer from high torque ripple and radial electromagnetic forces that cause vibration and noise. This paper proposes a game-theoretic multi-objective design optimization framework to enhance electromagnetic performance by simultaneously maximizing average torque and minimizing radial force. The optimization problem is transformed into a game model where objectives are treated as players with strategy spaces derived through fuzzy clustering and correlation analysis. Particle swarm optimization (PSO) is employed to solve the payoff functions under both novel cooperative and non-cooperative game scenarios of SRMs’ structural design. Finite element analysis (FEA) validates the optimized motor topology, showing that the cooperative game model achieves a balanced performance with high torque density and reduced vibration, meeting the requirements for air compressor drives. The proposed method effectively resolves the weight selection challenge in traditional multi-objective optimization and demonstrates strong engineering feasibility. Full article