Search Results (582)

Direct Torque Control (DTC) for induction motors (IMs) is an advanced method derived from Field-Oriented Control (FOC). In DTC, a voltage source inverter (VSI) is employed to directly regulate the stator flux linkage and electromagnetic torque through space vector modulation (VSM), where the optimal switching vector is selected for the VSI. Similarly to FOC, the stator flux and electromagnetic torque are independently controlled to deliver enhanced dynamic performance. However, DTC still suffers from certain drawbacks, such as slow transient response, limited dynamic performance, and high ripples in torque and flux. In this paper, an improved DTC method is proposed for a three-phase squirrel-cage induction motor. Specifically, a Type-2 fuzzy logic controller is employed to regulate both the stator flux and electromagnetic torque (T2FLC). The proposed method (FLCDTC) combines a three-level VSI with dual-band hysteresis (DBHW) switching to generate the gating signals for the insulated gate bipolar transistors (IGBTs). This approach effectively reduces the total harmonic distortion (THD) in torque and stator current, lowers the common-mode voltage (CMV), and enhances the overall motor performance. Simulation results under random noise distribution demonstrate the robustness of the proposed controller, even at low operating speeds. Finally, the effectiveness of the algorithm is validated in real-time through hardware-in-the-loop (HIL) implementation. Full article

Under ideal trapezoidal back electromotive force (EMF) conditions, a brushless direct current (BLDC) motor can produce constant instantaneous electromagnetic torque when supplied with ideal three-phase square-wave currents. However, this operating mode may result in relatively high copper loss. In practical applications, where both the back-EMF and the current waveforms deviate from their ideal shapes, significant torque ripple is introduced. To address these issues, this paper proposes a maximum torque per ampere (MTPA) control strategy for BLDC motors based on zero-sequence current injection. An improved Park (3s–3r) is employed to develop the mathematical model, in which the synthesized non-zero-sequence components are mapped exclusively onto the q-axis. By properly regulating the d-axis and 0-axis reference currents, the proposed strategy achieves minimum copper loss operation. Based on this framework, a torque control system incorporating zero-sequence current injection is established to further enhance performance. The feasibility and effectiveness of the proposed control strategy are validated through digital signal processing (DSP)-based experimental results. Full article

This paper presents an analysis of the impact of sub-cycle voltage reductions (below 1 ms) on the operation of a slip-ring induction motor. Due to the specific design of the slip-ring induction motor and the presence of a separate rotor circuit, direct measurements of rotor currents and voltages are possible, enabling a more detailed analysis of the physical phenomena occurring in the machine. A series of experiments was conducted using the Profline 2100 device, which enables the generation of controlled sub-cycle voltage reductions. This made it possible to directly assess the influence of such disturbances on motor operation, particularly changes in stator and rotor currents, rotational speed, and electromagnetic torque pulsations. The electrical and mechanical parameters of the motor were also identified. The obtained data were used to develop a mathematical model and implement it in the MATLAB/Simulink environment, enabling qualitative reproduction of the observed phenomena. The main novelty of this work is the analysis of the electromagnetic response of a slip-ring induction motor to sub-cycle voltage reductions below 1 ms, supported by direct measurements in the rotor circuit. The resulting model, validated against measurement results, shows qualitative agreement with the experiments and enables a more detailed analysis of motor dynamics during sub-cycle voltage reductions, including phenomena that are difficult to capture experimentally. Full article

The requirement of sustainable mobility and a clean environment has accelerated the development and adoption of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as an alternative, practical and promising solution against conventional vehicles globally. Such alternative energy vehicles not only provide a critical solution to mitigate fossil fuel dependency and reduce greenhouse gas emissions, but also contribute to producing an energy-efficient transportation system. However, the operational performance, efficiency, and cost-effectiveness of EVs and HEVs are hugely dependent on their powertrain architectures, selection of traction motors and associated control techniques. This paper systematically compares major hybrid architectures: series, parallel, and series–parallel, plug-in, as well as battery and fuel cell electric vehicle platforms, highlighting trade-offs in component sizing, cost, and system integration complexity. The paper critically analyses traction motor technologies with respect to torque–speed characteristics, efficiency behavior, material constraints, and power density. A detailed comparative assessment of traction motor technologies is presented. Furthermore, classical and advanced motor control strategies, including field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC) and AI-enhanced control frameworks, are evaluated with respect to transient performance, robustness, computational requirements, and scalability. The review identifies key technological milestones, emerging next-generation drive technologies, existing limitations, and unresolved research challenges. Finally, critical research gaps and future development pathways are articulated to support the advancement of high-efficiency, reliable, and cost-effective EV/HEV powertrain systems. Full article

The limited self-starting capability of H-type Darrieus Vertical-Axis Wind Turbines (VAWT) remains one of the main obstacles to their deployment in low-power and urban applications, where wind conditions are typically weak and intermittent. Although several passive geometric modification strategies have been proposed to enhance initial torque generation, most available studies rely predominantly on numerical simulations, with limited systematic experimental validation under low tip-speed ratio (TSR) conditions. In this work, the influence of passive blade modifications on self-starting performance is assessed through a combined numerical–experimental approach. An integrated numerical–experimental framework was used to systematically compare passive blade configurations under equivalent low-wind conditions. Two modified configurations, a biomimetic profile incorporating passive trailing-edge devices and an asymmetric J-type geometry, were optimized using transient CFD simulations of the first rotation cycle and a Design of Experiments (DOE) framework. Additively manufactured full-rotor test blades were then manufactured via additive manufacturing and tested in a controlled wind tunnel at 3.0 m/s and 2.25 m/s. Start-up time, azimuthal robustness, tip-speed-ratio evolution, and static start-up torque (interpreted through its corresponding torque coefficient) were measured and compared against a baseline NACA0018 profile. The biomimetic configuration consistently produced higher start-up torque and shorter acceleration times, achieving self-starting in 66.7% of the evaluated azimuthal positions at 2.25 m/s, compared to 22.2% for the baseline profile. Within the investigated operating range, this configuration emerged as the most robust passive strategy. The agreement between CFD predictions and experimental measurements supports the use of first-cycle maximum torque as a representative indicator of self-starting performance. These findings highlight the comparative value of first-cycle maximum torque as a practical metric for passive self-starting design assessment in low-TSR Darrieus turbines. These findings provide direct experimental evidence to guide the rational design of Darrieus turbines intended for marginal wind conditions. Full article

At racing speeds above 300 km/h (≈83 m/s), hazard awareness becomes a vehicular-communications problem: 100 ms already correspond to about 8.3 m of blind travel before an alert can influence braking, line choice, or torque delivery. Cloud-only telemetry is therefore insufficient under intermittent coverage and variable round-trip delay, while conventional trackside and pit-wall links do not provide direct inter-bike hazard dissemination. We propose Hybrid Epistemic Offloading (HEO), an edge–mesh–cloud architecture for high-mobility V2V/V2X hazard dissemination that explicitly separates an ephemeral safety plane from a durable cloud-analytics plane. On-bike edge nodes ingest high-rate ECU/IMU signals over CAN and persist full-fidelity traces into standardized ASAM MDF containers, enabling loss-tolerant buffering, deterministic replay, and post hoc auditability across coverage gaps. For real-time safety, motorcycles form a local V2V mesh that disseminates compact hazard digests using latency-bounded gossip with adaptive fanout, TTL-based suppression, and redundancy-aware forwarding over sidelink-capable V2X links. The hazard channel is formulated as uncertainty-aware to account for localization error and propagation delay at race pace. We evaluate the system in two stages: (i) a reproducible mobility-coupled simulation/emulation campaign for mesh dissemination and durable edge → gateway → cloud delivery; and (ii) an MDF4 replay-based Jerez pilot for stability-oriented co-design analysis. Under the tested conditions, the durable MQTT path achieved an 83.4 ms median, 175.9 ms p95, and 303.74 ms maximum end-to-end latency with no observed event loss. In the Jerez pilot, the co-design workflow reduced mean wheel slip from 6.26% to 3.75% (−40.10%) and a control-volatility proxy from 0.1290 to 0.0212 (−83.58%). Full article

Objective: The objective of this study is to evaluate the biomechanical effects of different attachment placement strategies using rectangular attachments on palatal root torque control of maxillary central and lateral incisors with clear aligners. Methods: Three-dimensional finite element analysis was performed to simulate simultaneous 1° palatal root torque of maxillary central and lateral incisors. Six attachment configurations were evaluated: no attachment (control), canine-only, both incisors, central incisor-only, lateral incisor-only, and all anterior teeth. Three-dimensional tooth displacement and torque expression were analyzed across 200 iterative simulations. Model validation was confirmed through mesh convergence analysis and comparison with published studies. Results: Only the control and canine-only groups simultaneously achieved the appropriate torque direction for both incisors. Attachments on central incisors produced reverse torque, with the central incisor-only group showing the most severe magnitude, while the control and canine-only groups achieved expected directions, validating model reliability. Lateral incisors exhibited different responses, including reverse torque in the lateral incisor-only group. The canine-only attachment demonstrated the most balanced torque expression. Increasing anterior attachments was associated with greater extrusion and canine displacement. Conclusions: Attachment placement using rectangular attachments significantly influenced torque expression during palatal root torque. Central and lateral incisors responded differently to attachments, and certain configurations produced reverse torque. For small torque movements (1–2°), a “less is more” approach using rectangular canine attachments for anchorage proved most effective, suggesting that anchorage may be more critical than incisor attachments for anterior torque control. Full article

To address the strongly nonlinear hydrodynamic coupling and complex maneuvering challenges encountered by large ships during berthing operations in restricted waters, this paper proposes a high-precision autonomous berthing control system incorporating four-quadrant propeller hydrodynamics. Based on an improved Mathematical Maneuvering Group (MMG) framework, a three-degree-of-freedom (3-DOF) dynamic model is established to accurately capture the transient thrust and torque mappings of the propeller over all four quadrants. A dynamic line-of-sight (LOS) guidance system with a nonlinearly decaying acceptance radius is tightly coupled with PD/PI controllers to coordinate and regulate the rudder angle and propeller rotational speed. The numerical solver was rigorously validated against turning-test data for the S-175 container ship, with the errors of the key parameters all controlled within 15%. Subsequently, under the environmental conditions of Yangshan Port, full-condition path-planning and berthing simulations were conducted for the novel B-573 container ship under steady-current disturbances. These simulations evaluated multiple flow directions, namely due south, due north, due west, and due east defined in the Earth-fixed coordinate system, as well as multiple intensity levels ranging from 0 to 1.5 m/s that were specifically tested under the due north current. Quantitative evaluation shows that, under the highly challenging current condition of 1.0 m/s, the dynamic corrective mechanism effectively drives the global mean absolute error (MAE) to converge to 85.50 m, representing a 62% statistical reduction relative to the transient peak value. In addition, a parameter sensitivity analysis based on the cumulative cross-track error confirms that, when subject to variations in the underlying hydrodynamic parameters, the proposed system can suppress fluctuations in trajectory error to a very low level, thereby demonstrating a certain degree of control robustness. During the terminal berthing stage, the vessel smoothly completed an extreme deceleration from an initial speed of 6.4 m/s to a full stop within 588 s, while constraining the maximum astern rotational speed to −2 rps and seamlessly passing through all four propeller quadrants. The results confirm that the proposed autopilot framework possesses a certain degree of engineering feasibility in complex maritime environments. Full article

When the Risley prism pair is used for target tracking, the nonlinear relationship between beam deflection and prism rotation makes tracking performance highly dependent on precise and stable motor control over a wide speed range. Although the brushless DC motor serves as the preferred drive source, its inherent commutation torque ripples directly induce beam pointing jitter, severely degrading overall tracking accuracy and stability. To address these issues, this paper proposes a three-vector-based model predictive direct speed control method. This approach establishes a direct speed-to-torque control channel by generating reference active power through dynamic equations, eliminating the need for fitting a constant flux linkage and parameter tuning. Simultaneously, combined with three-vector optimization and seven-segment modulation strategies, it achieves a dynamic balance between high-frequency, instantaneous electromagnetic power fine-tuning and inherent mechanical inertia of the rotor. Simulation results demonstrate that the proposed method exhibits superior speed stability compared to the conventional double-vector-based model predictive power control method and maintains high-precision dynamic tracking over a wide speed range. Ultimately, it leads to an average reduction of over 60% in the time-weighted absolute tracking error integral under various target trajectories, providing an effective solution for drive control of target tracking in Risley prism systems. Full article

Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In this work, we propose a Single-Direction CNN-LSTM decoder inspired by motor cortex encoding mechanisms, which separately models extension and flexion dynamics through parallel CNN-LSTM branches. Each branch extracts spatial–temporal features from neural spike data and predicts directional joint variables, which are then combined by subtraction to yield the net angular velocity and torque of upper-limb joints. Using invasive recordings from a macaque during a 2D center-out reaching task, we demonstrate that our decoder achieves comparable performance to a conventional CNN-LSTM when trained on all tasks, while significantly outperforming both CNN-LSTM and linear regression baselines in cross-target generalization scenarios. Moreover, the model can capture physiologically meaningful co-contraction patterns, providing richer insights into motor control. These results suggest that incorporating neuroscience-inspired modular decoding into deep neural architectures enhances robustness and adaptability across tasks, offering a promising pathway for BMI applications in prosthetics and rehabilitation. 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

Accurate estimation of axle torque is essential for performance evaluation and energy management of electric tractors. However, direct torque measurement and access to motor controller data are often limited in commercial platforms. This study proposes a machine learning-based framework for predicting axle torque in a commercial electric tractor using field-measured sensor signals. The framework incorporates a horizon-aware architecture to capture the temporal dependencies of dynamic load fluctuations. Field experiments were conducted during plow tillage operation under multiple gear–speed combinations. Several machine learning models (multiple linear regression, multilayer perceptron, and CatBoost) were evaluated for axle torque prediction. The results showed that rear axle torque exhibited a stronger relationship with traction demand under two-wheel-drive operation, resulting in higher prediction accuracy than front axle torque. Among the evaluated models, CatBoost achieved the best overall performance, with an $R^2$ of 0.83 and an RMSE of 189.35 Nm for the rear axle prediction. The proposed framework enables real-time axle torque estimation using commonly available sensor signals and provides a practical alternative to direct torque measurement for onboard load monitoring and energy management in electric tractor systems. Full article

This paper presents the design, simulation, and experimental validation of a novel type of pneumatic rotary positioner that is based on a three-cylinder radial mechanism driven by independently controlled pressures. The system uses standard off-the-shelf industrial components, including pneumatic cylinders, proportional pressure regulators, and a programmable logic controller. In order to obtain angular positioning, a three-phase sinusoidal pressure commutation scheme is adopted, similar to the three-phase electrical motors. Analytical expressions for piston kinematics and torque generation are derived and used to design direct open-loop, open-loop with friction compensation, and closed-loop position control strategies. The technical implementation, with the prototype tested unloaded, can achieve accurate positioning (±3° in open-loop mode with feedforward to ±0.3° in closed-loop mode with PD controller), with very good repeatability on average (<0.5°) and smooth theoretical torque (average 1.4 Nm, with 0.51% ripple) at low speeds (<60 rpm). The experimental prototype was designed as a compact device, having approx. 94 mm diameter and 110 mm depth. When used in open-loop mode, the actuator is connected to the control system using just three pneumatic tubes and thus is completely free of any electromagnetic fields, making it suitable for some environment-critical applications. These advantages promote the proposed positioner as a practical rotary actuator in specialized automation and robotics applications where established electrical servomotors cannot be used. Full article

BLDC motors are utilized in electric cars, robotics, drones, home appliances and medical equipment due to their effectiveness, dependability, and accurate control. PI controllers have been put forward to enhance the dynamic performance of brushless direct current (BLDC) motors, and they have been tested in many papers with various algorithms (such as PSO, GA, GWO, ACO and ABC) and strategies (such as PI/PID control, FOC, FLC, SMC and MPC). Meanwhile, in this research, and for the first time, the PI controller was tuned by the proposed Sailfish Optimization algorithm (SFO) with a direct torque control (DTC) strategy to enhance the dynamic performance of BLDC motors. Although DTC provides a very fast torque response, it still suffers from high torque ripple and noticeable instability at low speeds. These issues persist even when using conventional PI tuning or common optimization algorithms. Hence, in this research, we proposed an improved control strategy that combines DTC with PI tuning optimized by the Sailfish Optimization algorithm (SFO), which delivers smoother torque, more stable low-speed operation, and stronger robustness during sudden changes in load. In this regard, the PI controller was tested under different levels of torque and compared with the traditional Gray Wolf Optimization (GWO-PI) algorithm controller, as well as PI and PID controllers, and the performance of each of them was evaluated for different torque levels at speeds of 600 rpm and 2000 rpm during physical experiments. The simulation results showed that the Sailfish-PI controller, compared to the others, recorded the fastest response with a rise time of 2.1 ms and settling time of 2.9 ms under 2.39 Nm nominal torque at 2000 rpm speed; in addition, it continuously showed the lowest values of overshoot and undershoot as torque increased. It also maintained the most accurate and consistent performance, keeping the peak rpm almost flat and extremely near to the target of 2001 rpm. Therefore, in systems that require variable speed and torque while operating, such as electric automobiles, the proposed method is suitable for application. Full article

Six-dimensional force sensors are widely used in compliant robotic control and safe human–machine interactions due to their mature sensing mechanisms and high accuracy. However, conventional six-dimensional force sensors often suffer from complex structures, bulky size, and high manufacturing costs. To address these limitations, this paper proposes a compact and low-cost six-axis force sensor based on capacitive sensing. By employing a tailored arrangement of flexible sensing units, partial structural decoupling of force and torque in specific directions is achieved. A Physically Informed Neural Network (PINN) is further introduced to decouple the residual coupled signals. Experimental results demonstrate that the proposed method significantly improves decoupling accuracy, achieving force decoupling errors of 1.75%, 1.20%, and 1.31% for F_x, F_y, and F_z, respectively, and torque decoupling errors of 0.95%, 0.93%, and 0.97% for M_x, M_y, and M_z. The proposed sensor offers low-cost fabrication, compact integration, and high sensitivity, making it well suited for lightweight and high-precision sensing applications. Full article