Scalable Observer Bandwidth Based Active Disturbance Rejection Control of Brushless DC Motor

To precisely control the speed of BLDC motors at high speed and with very good performance, an accurate motor model is required. As a result, the controller design can play an important role in the effectiveness of the system. The classic controllers such as PID are widely used in the BLDC motor controllers, but they are not appropriate due to non-linear model of the BLDC motor. To enhance the performance and speed of response, many studies were taken to improve the adjusting methods of PID controller gains by using fuzzy logic. Use of fuzzy logic considering approximately interpretation of the observations and determination of the approximate commands, provides a good platform for designing intelligent robust controller. Nowadays type-2 fuzzy logic is used because of more ability to model and reduce uncertainty effects in rule-based fuzzy systems. In this paper, an interval type-2 fuzzy logic-based proportional-integral-derivative controller (IT2FLPIDC) is proposed for speed control of brushless DC (BLDC) motor. The proposed controller performance is compared with the conventional PID and type-1 fuzzy logic-based PID controllers, respectively in MATLAB/Simulink environment. Simulation results show the superior IT2FLPIDC performance than two other ones.

This paper presents the groundwork for a novel concept of sensorless direct speed control (SDSC) in brushless DC (BLDC) motor drives. Unlike the conventional sensorless speed controls, the proposed strategy does not need proportional plus integral (PI) regulators. Thus feedback mechanism is minimized. Speed regulation is achieved by controlling rotational speed of stator flux linkage. The PI regulator is replaced with a simple comparator. The BLDC motor is excited based on a look-up table. This proposal is applicable to both two-phase and three-phase conduction modes of the BLDC motors. In this paper, principles of the proposed sensorless strategy and speed ripple calculation are described in details. The validity of this claim is verified through the simulation results.

This paper presents position sensorless control for brushless DC motor based on a commercial application-specific integrated circuit (ASIC) ML4425. ML4425 is often used for sensorless control of brushless DC motor. The position detection scheme based on terminal voltage sensing and the controller function of ML4425 is analyzed. The control system based on ML4425 will be developed. The experimental results is received and discussed as well.

A sensorless drive scheme with adaptive gain speed controller for brushless DC motors is proposed in this paper. There are three observers in the control system, they are a disturbance voltage observer, a speed observer, and a disturbance torque observer. These observers are used not only for a position and speed estimator in the sensorless vector control, but also for a current controller. The rotor position and speed estimator are robust for parameter variations, such as the e.m.f. constant. The validity of the sensorless vector control of brushless DC motors is confirmed with computer simulations.

As the character of brushless DC motor (BLDCM) is nonlinear, the classical control method can not obtain satisfied performance, so the fuzzy control is used to control BLDCM, but the conventional fuzzy controller has no integral part, which performance is similar to Proportional Derivative(PD) controller, so its static control precision cannot be guaranteed. In this paper, a fuzzy Proportional Integral and Derivative(PID) controller that can ensure static precision is proposed to improve the performance of conventional fuzzy controller and is applied to the speed control of brushless DC motor. The proposed fuzzy-PID method employs fuzzy control when the error is big, employs the PID control when the error is small, so it has both the rapid response of ordinary fuzzy controller and the steady precision of PID controller. The experiment results show the dynamic and static performance of the proposed fuzzy-PID controller are all excellent, the overshoot is zero, the speed respond is fast and the static precision is high, better than the conventional PID controller and the conventional fuzzy controller in speed control.

Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor

This paper presents a robust fuzzy proportional-integral-derivative (PID) controller for brushless DC motor (BLDCM) control system. The hardware circuit of the BLDCM control system is designed and implemented using a digital signal processor (DSP) TMS320LF2407A and a monolithic BLDCM controller MC33035 as the core. Furthermore, a fuzzy PID controller, which combines the advantages of good robustness of fuzzy controller and high precision of conventional PID controller, is employed in the hardware system, thereby yielding a digital, intelligent BLDCM control system. Experimental results have shown that the control system can run steadily and control accurately, and have convincingly demonstrated the usefulness of the proposed fuzzy PID controller in BLDCM control system.

In this paper, a model predictive current control (MPCC) method based on pseudo-dq transformation is proposed for maximum torque per ampere (MTPA) control of the brushless dc motor (BLDCM). A current predictive model of BLDCM in discrete steps is built. A cost function considering the torque current tracking and the MTPA operation is introduced. The optimal voltage vector to control the motor is then selected according to the cost function. Additionally, to improve the robustness of the system, a sliding mode control is applied to the outer speed control loop. In the end, the simulation results validate a better performance of the proposed control scheme compared to the traditional control method.

A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC motor is the basic part of the small satellite attitude control flywheel. In order to accurately control the attitude of the small satellite, a good motor control system is very important. Firstly, the mathematical model of the BLDC motor is established and the BLDC motor speed control system using traditional PID control is designed. Secondly, considering that the small satellite speed control system is a nonlinear system, a fuzzy PID control is designed to solve the shortcomings of the fixed parameters of the traditional PID control. Finally, we find that the control accuracy of the fuzzy PID control will change with the range of the input. Therefore, we introduce the AEFA to optimize fuzzy PID to achieve high-precision attitude control of small satellites. By simulating the BLDC motor system, the proposed fuzzy PID controller based on AEFA is compared with the traditional PID controller and the fuzzy PID controller. Results from different controllers show that the proposed control method could effectively reduce steady state error. In addition, the proposed fuzzy PID–AEFA controller has the better anti-jamming capability.

This paper presents an advanced sensor-less control for a brushless DC motor (BLDCM) based on twelve step drive scheme. In twelve step drive, the large torque ripple is caused by the current commutation between three phase windings of BLDCM. To solve the problem, this paper proposes the advanced twelve-step sensor-less control to reduce torque ripple. The proposed control has two modes. In two phase commutation mode (TWPCM), the rotor position of BLDCM is estimated by zero crossing point of back EMF and the reference value of duty ratio to control speed of BLDCM is calculated. In three phase commutation mode (THPCM), torque ripple is reduced using the reference duty value that calculated by TWPCM and three phase currents that measured by a single shunt resistor. The effectiveness of the proposed control is verified by the simulation and experiment according to the comparison of the output characteristics between the traditional controls and proposed control.

Brushless DC Motors (BLDC) are finding wide applications in household appliances and electrical vehicles. Microcontroller are widely used for controlling the BLDC motors however the realization of BLDC drive requires expertise in microcontroller programming. In this paper modelling, simulation and easy hardware implementation of BLDC motor having shaft encoder using STM32F4 series ARM Cortex-M4 microcontroller is demonstrated. A simple method of controlling the BLDC motor in closed loop, using a hysteresis current controller is implemented. The hall sensors are used to find the exact rotor position for the electronic commutation and the encoder is used to measure actual motor speed. The closed loop operation consists of an inner loop which is hysteresis current controller. The gate pulses for the three phase inverter bridge are produced by comparing the actual currents with the reference currents. The outer speed loop consists of a PI controller which compares the actual speed with the reference speed to obtain smooth speed control under varied load condition. The complete hardware circuits and MATLAB/Simulink environment blocks for programming are discussed in detail. The hardware results demonstrate the easy and accurate realisation of closed loop speed control of BLDC Motor.

This paper presents an improved emotional controller for brushless DC motor (BLDC) drive. The proposed controller is called brain emotional learning based intelligent controller (BELBIC). The utilization of the new controller is based on the emotion processing mechanism in brain. This intelligent control is inspired by the limbic system of mammalian brain, especially amygdala. The controller is successfully implemented in simulation using MATLAB software, brushless dc drive with trapezoidal back-emf. In this work, a novel and simple implementation of BLDC motor drive system is achieved by using the intelligent controller, which controls the motor speed accurately. This emotional intelligent controller has simple structure with high auto learning feature. Simulation results show that both accurate steady state and fast transient speed responses can be achieved in wide range of speed from 20 to 300 [rpm]. Moreover, for evaluate this emotional controller and hence to assess the effectiveness and control capability of the proposed BELBIC scheme, the performances of proposed control scheme are compared with a conventional PID controller for the BLDC drive control, in simulation different conditions. This is shown proper operating than the PID controller. And also shows excellent promise for industrial scale utilization.

This paper presents a notion of speed control of brushless DC (BLDC) motor through a compound control technique using CMAC (cerebellar model articulation controller) and PID controller. The proposed system provides effective starting and diminished cost and core loss. Compound controller realizes the dual-fed control which ensures stability and convergence of system. The initial rotor position is determined by estimating the voltage on sample resistor moreover the core loss are significantly diminished by using the ferrite material in stator core. The benefits like high control accuracy, increased efficiency, adaptability, decreased startup time and increased starting torque are attained in presented system.

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors.

This paper presents the design and analysis of a Grey Wolf Optimization (GWO)-based PI controller for a solar-fed Brushless DC (BLDC) motor with Incremental Conductance (INC) MPPT technique. Conventional MPPT methods such as Perturb and Observe (P&O) and PSO suffer from steady-state oscillations, slower tracking, and limited optimization performance. The proposed system integrates the INC MPPT technique with GWO-tuned PI controller for enhanced performance. The solar PV array is connected to a DC-DC boost converter and three-phase inverter feeding the BLDC motor. The INC MPPT method ensures accurate and fast maximum power point tracking with reduced oscillations under varying environmental conditions, while the GWO algorithm optimally tunes the PI controller parameters by minimizing the Integral of Absolute Error (IAE). MATLAB/Simulink simulation demonstrates 99.4% MPPT tracking efficiency, 1.0% steady-state speed error, 35ms settling time, and 38% reduction in IAE compared to conventional PSO-tuned controllers, validating the approach for solar-powered electric drives in water pumping and EV applications. Keywords: BLDC Motor, Solar PV, Grey Wolf Optimization, INC MPPT, PI Controller, Speed Control, Renewable Energy Drive