Résultats de recherche pour « BLDC Motor Controllers »

Brushless DC (BLDC) motors are increasingly used in various industries due to their reliability, low noise, and extended lifespan compared to traditional DC motors. Their high torque-to-weight ratio and impressive starting torque make them ideal for automotive, robotics, and industrial applications. This paper explores the multi-objective tuning of BLDC motor controllers, focusing on position and torque ripple. A state-space model of the BLDC motor and the entire control system, including the power stage and control structure, is developed in the Simulink environment. Two common control mechanisms, trapezoidal and Field Oriented Control (FOC), are implemented and optimized. Both mechanisms utilize a cascaded closed-loop position control, providing fair disturbance rejection but requiring challenging tuning of the controllers. To address these challenges, the non-dominated sorting genetic algorithm II (NSGA-II) is used for optimization. This study demonstrates the effectiveness of optimization techniques in enhancing the performance of control systems.

Artificial Neural Network (ANN) is a simple network that has an input, an output, and numerous hidden layers with a set of nodes. Implementation of ANN algorithms in electrical, and electronics engineering always satisfies with the expected results as ANN handles binary data more accurately. Brushless Direct Current motor (BLDC motor) uses electronic closed-loop controllers to switch DC current to the motor windings and produces the magnetic fields. The BLDC motor finds various applications owing to its high speed, low maintenance and adequate torque capability. They are highly preferred than the other motors because of their better performance and it is easy to control their speed by Power Converters. This article presents a method of speed control of BLDC motors where speed is controlled by changing the DC input voltage of the bridge converter that feeds the motor winding. The control is done by using a PI based speed controller. The motor is modeled in the MATLAB/Simulink and the speed control is obtained with a PI controller. EMF signals, rotor speed, electromagnetic torque, Hall Effect signals, PWM and EMF signals simulations are then obtained. This acquired data is then fed into binary artificial neural networks and as a result, the ANN model predicts the corresponding parameters close to the simulation results. Both the mathematical based simulation and data based prediction gives satisfactory results

Accurate dynamics modeling of Brushless DC (BLDC) motors is fundamental to high-performance robotic joint control. This paper presents a Physics-Informed Neural Network (PINN) with a deep residual (ResNet) backbone that learns a continuous-time surrogate of the full six-state BLDC motor dynamics. Given simulation time, applied three-phase voltages, and excitation parameters as inputs, the network directly predicts all motor state variables -- rotor angle, angular velocity, three-phase currents, and winding temperature -- while simultaneously satisfying the governing electromechanical and thermal ODEs through a composite physics-data loss. A curriculum scheduling strategy gradually activates the physics penalty to prevent premature convergence. Training runs are completed in under two minutes on a standard CPU. Crucially, once trained, PINN inference achieves latencies of 0.1--22, mu s per query, up to 118x faster than conventional ODE solvers, making it suitable for real-time observer and control applications.

Multirotor unmanned aerial vehicles (UAVs) rely on electronic speed controllers (ESCs) that decode motor commands from pulse-width modulation (PWM) signals, making the flight-controller-to-ESC command path a physical-layer attack surface for intentional electromagnetic interference (IEMI). This paper presents a mechanism-based analysis of IEMI attacks that induce motor stoppage in UAV brushless DC motor controllers. We develop a timing-error model in which a sinusoidal disturbance on the PWM line shifts the detected edge instants and drives the decoded pulse width into stop-equivalent regimes, and we show that the disturbance reaching the ESC’s thresholding node is shaped by a frequency-selective cascade of the PWM cable’s coupling response and the ESC’s input-path transfer function. We experimentally characterize this model on five commercial ESCs through conducted and radiated injection. The measured thresholds differ by more than an order of magnitude across ESCs and are reordered between frequency bands and injection modes; comparing conducted and radiated results allows us to attribute these differences primarily to the cable coupling response and reveals cases where it either hides or amplifies an ESC’s susceptibility. The susceptible frequency also shifts with PWM cable length in qualitative agreement with transmission-line resonance, confirming that observed radiated susceptibility reflects the joint design of ESC and cable rather than a single intrinsic property. The cable lengths examined here (45–125 cm) are longer than those of compact multirotors and were chosen to place resonances within our antenna’s band; we discuss the implications of this choice and identify shorter, deployment-realistic cables as a priority for future work.

The performance improvement of high-power flat BLDC motors has accelerated the development of dynamic robots. However, many commercially available servo motors assume operating voltages of 48 V or lower, which limits the maximum rotational speed. Dynamic robots require rapid energy generation, so this voltage constraint restricts motion performance. Therefore, driving motors beyond the rated voltage is desirable to increase the instantaneous maximum speed. On the other hand, semiconductor devices used in motor drivers have a trade-off between voltage rating and current capacity. Conventional drivers using Si MOSFETs have difficulty achieving both high-voltage and high-current operation. Although GaN FETs are promising, compact drivers that can be mounted on the rear side of flat BLDC motors remain limited. In this study, a motor driver for high-power flat BLDC motors using GaN FETs is developed. The effect of delay compensation in the high-speed region beyond the rated operating range is also investigated. In the experiments, under 96 V operation, a continuous current of 30 A was achieved with a heat sink attached. A peak current of 80 A and a maximum electrical frequency of 3110 Hz were confirmed. A high-speed load lifting experiment driven by a LiPo battery 24S (100 V) was also conducted, demonstrating applicability to dynamic robot operation.

This diploma thesis documents the conversion of a petrol-powered go-kart into a battery-electric kart with a 10 kW BLDC drive, a LiFePO$_4$ traction battery with battery management system, a slip-based traction control and regenerative braking. The traction control estimates the slip of the driven rear axle from three Hall-effect wheel-speed sensors as $λ= (v_R - v_F)/v_R$ and reduces the torque command of the throttle pedal when the axle slips. It runs on a 32-bit ARM Cortex-M3 microcontroller (SAM3X8E) in the signal path between pedal and motor controller, with galvanic isolation and an emergency stop. Regenerative braking uses the generator mode of the motor controller in three stages: motor brake on throttle release, light braking via a steering-wheel button, and blended braking with the hydraulic brake. Drivetrain calculations show that the kart is traction-limited. At rated torque, the drive force at standstill exceeds the transmissible traction force by a factor of 1.7 to 2.0, up to approx. 27 to 43 km/h. Traction control is therefore a functional requirement of the drivetrain. The calculated top speed is approx. 65 km/h. Regenerative braking reaches approx. 0.45 g and could cover up to about 30 % of the energy demand on a winding track. With 80 % of the nominal battery energy, approx. 44 min of driving at 50 % motor load result, so the target of one hour is missed. The initial charging measurement quantifies the flat LiFePO$_4$ voltage plateau at 0.7 to 1.8 mV per percent state of charge, which makes voltage-based state-of-charge estimation unreliable. Cold cells showed a voltage sag under load that triggered the low-voltage alarm. Validation of the control functions by instrumented driving tests is the main open point.