Search Results for « BLDC Motor Controllers »

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.

Due to their better electrical properties and mechanical features, the usage of BLDC motors is increasing and they have replaced conventional DC and asynchronous motors in many applications. This project provides a sensor-based permanent magnet brushless DC motor for commercial and electric vehicles. To improve the reliability of BLDC motor speed regulation, a PID controller is employed. The three-phase voltage inverter delivers a constant DC voltage and is synced with the BLDC motor. Closed-loop speed control is done utilizing the Ziegler-Nichols technique. Particle Swarm optimization and PID controller improved using Firefly algorithm The recommended system is simulated using MATLAB simulation and the performance of the BLDC motor is tested by comparing attributes such as Rise time. Settling time and Peak overshoot.

While the Internal Model Principle (IMP) and Disturbance Observer (DOB) are fundamental to robust control, their systematic equivalence within a unified framework has received limited attention. IMP-based control achieves robustness through the structural inclusion of signal generators, whereas DOB-based methods rely on extended state representations for disturbance estimation. This paper bridges this gap by designing a state-space Reduced-Order Disturbance Observer (RODOB)-based controller that achieves systematic equivalence with an IMP-based transfer function controller. As a design example, an IMP-based controller is synthesized using a Linear Quadratic Regulator (LQR) for an augmented system in error space, with reference inputs directly integrated into the RODOB structure to eliminate the need for additional filters. Simulations and hardware experiments on a Brushless DC (BLDC) motor verify that both structures exhibit consistent control input and output characteristics, significantly outperforming conventional cascade and PID strategies. Numerical stability during digital implementation is ensured via partial fraction expansion. Furthermore, a method for estimating equivalent disturbances—encompassing both external loads and model uncertainties—is proposed by leveraging RODOB states. These findings suggest significant potential for future applications in fault diagnosis and real-time condition monitoring.

BLDC motors have efficiency, miniaturization, and low maintenance requirements that make them a crucial aspect of modern electric drive systems. Inherent nonlinearities and parameter uncertainties, together with high torque and speed transitions, generally worsen the performance of conventional controllers in a BLDC system. In this regard, this work proposes a Cascaded Hybrid Soft Computing Controller that merges DRL with Fuzzy SARSA(λ) learning for intelligent, adaptive, and data-driven control of BLDC motors. A cascaded architecture combines the global policy optimization of DRL with the fuzzy rule-based adaptability of fuzzy reinforcement learning to guarantee real-time stability with quicker convergence and higher learning accuracy. Simulation output shows the superior transient and steady-state performance of the developed hybrid controller. The developed method registers a settling time of 0.28 s, steady-state speed of 1498 RPM, torque ripple of less than 2.95 N·m, and remarkably stable DC-link voltage around 300 V when compared to Fuzzy SARSA(λ)-only and MPC control strategies. These results confirm that the controller has superior robustness, adaptability, and accuracy under speed reversals and sudden load disturbances. The resulting cascade scheme therefore offers a strong direction for future intelligent motor drives and autonomous energy-efficient actuation systems.

This study explores the performance of Proportional-Integral-Derivative (PID) and Linear Quadratic Regulator (LQR) controllers in regulating BLDC motor speed. The PID controller is tuned using both MATLAB-based auto-tuning and manual parameter selection, while the LQR controller is designed by adjusting the Q and R matrices through iterative optimization. To evaluate their effectiveness, a mathematical model of the BLDC motor is developed in a state-space representation, enabling simulation of its dynamic response. The open-loop system shows a slow response and significant steady-state error, making it unsuitable for precise speed control. The PID tuner improves response time and accuracy, it introduces overshoot. Manual tuning further refines performance by reducing overshoot and enhancing stability. The LQR controller demonstrates the fastest response, minimal overshoot, and the lowest steady-state error, making it the most effective approach for BLDC motor speed regulation