Advancements in PMSM-Based Commercial Fan Control: Hardware, FOC Implementation, and Performance Evaluation ^†

Abstract

This work explores permanent-magnet synchronous motor (PMSM) technology and its control methods, focusing on field-oriented control (FOC) for precise motor control. It discusses hardware development using ORCAD and MATLAB/Simulink for modeling, analysis, and code generation. Recent technological trends in PMSM drive applications, including variable-frequency drive (VFD) control, are highlighted alongside performance evaluations. The use of the Embedded Coder toolbox in Simulink is emphasized for efficient C code generation for embedded targets like DSP controllers. Overall, this paper presents a comprehensive overview of PMSM control methodologies and their practical implementations.Rudra

1. Introduction

The transition from electromagnets to permanent magnets in DC machines led to compact, high-energy designs, enabling widespread use of brushless DC and permanent-magnet synchronous motors. Electronic commutators replaced mechanical ones, enhancing operation and speed–torque characteristics. Power electronics drives crucially boost performance, leveraging permanent-magnet field excitation. Simplified construction and reduced electrical losses from the absence of rotor windings contribute to the popularity of these motors, while Hall sensors and advanced controllers enhance stability and performance.

In today’s fast-paced world, permanent-magnet synchronous motors (PMSMs) are crucial in industrial automation, traction, and aviation, demanding greater power and intelligence. PMSM combines features of induction and brushless DC machines, boasting higher power density than induction motors due to efficient magnetic field utilization. Designed for enhanced power, lower mass, and compactness, modern PMSM motors feature a sinusoidal flux strength akin to induction machines, ensuring efficient operation in various applications [1]. The motor employs sinusoidal flux distribution and utilizes vector control for modeling and analysis. Performance comparisons between pulse width modulation (PWM) and hysteresis current controllers were conducted for the motor’s operation [2]. The drive system utilizes cascade control with speed feedback for machine rotation. Enhanced vector controller techniques for interior PM synchronous engine drives were developed and validated through experimental reports [3]. Discrete control frames adjust current reference signals using progressive speed estimations. A two-hub circuit model for PMSM considers magnetic imbalance and core loss, with parameter recognition for saturation constants [4]. High-frequency, high-precision estimations are vital for reducing compliant electrical losses in PM motor drive systems. Complex algorithms optimize current vector control, evaluated through computer simulations and experimental analysis [5]. PMSMs excel in high-speed applications, focusing on minimizing losses and maximizing torque density. Motor parameters are finely tuned based on design principles, with enhanced algorithms optimizing key factors for efficient operation [6]. Coreless axial flux PMSM machines are optimal for high-speed, low-power operations, aiming to reduce torque ripple at around 30,000 rpm. Utilizing a Halbach cluster construct for the rotating part enhances axial pull and ensures a lightweight yet sturdy design in the preferred axial flux motor [7]. FOC optimizes PMSM operation by decoupling flux and torque components. A closed-loop FOC algorithm is implemented on a 60 W PMSM using TI Launchpad f28069M and DRV8301 booster pack, with auto-generated code via the MATLAB embedded coder [8]. MATLAB and Simulink are integral tools for unified development and real-time performance analysis of electric motor drive systems, enabling rapid prototyping and testing of controller circuits with motor models [9].

This paper presents a solution for constrained control of a permanent-magnet synchronous machine, validated experimentally on a Texas Instruments microcontroller with a surface-mounted PMSM [10]. The paper introduces an efficiency optimization controller for PMSM electric vehicle motors, utilizing a new loss model and a novel loss minimization algorithm based on the energy balance equation and the Hamiltonian system principle [11]. This study conducts multiphysics modeling and analysis of a high-speed, low-power axial flux permanent-magnet motor with a multilayer PCB stator, validated through MagNet 3-D and COMSOL Multiphysics simulations and prototype testing [12]. This paper presents an improved winding design for a disc-type permanent-magnet motor with a PCB stator, enhancing motor performance and stator utilization through analytic expressions and finite-element simulations, validated by a prototype [13]. This study evaluates various winding topologies for PCB axial flux machines, demonstrating the superiority of parallel winding in minimizing resistance and loss while maximizing induced voltage and torque, with the radial winding showing reduced phase resistance and total harmonic distortion [14]. This paper demonstrates the modeling, analysis, and realization of current vector control for a PMSM drive using MATLAB/Simulink and FPGA to achieve faster response compared to low-cost DSPs [15]. This article introduces machine learning-based controllers for surface PMSM drive systems, which outperform conventional PI controllers with a 20% improvement in speed tracking and 0.02% reduction in transient levels [16].

Brushless motors offer improved efficiency by controlling velocity through current frequency rather than voltage, eliminating mechanical energy loss associated with commutators. Stepper motors, while precise, suffer from drawbacks such as high acceleration times, noise, and limited speed and resolution, prompting a shift to PMSM machines. PMSM motors, utilizing Hall sensors for position feedback, offer high-speed operation, reduced noise, and enhanced cooling, with vector control enabling precise speed regulation and high torque performance over a wide speed range.

The primary objective of this work is as follows:

• Utilize Hall sensors for precise speed and position control in controlling techniques;
• Develop hardware circuitry and implement field-oriented control (FOC) with space vector modulation in MATLAB simulation;
• Analyze VFD drive tuning and evaluate performance on a PMSM motor.

2. Methodology

2.1. Design and Simulation of Control Algorithm

This section analyzes the vector control of the three phases of a PMSM. The mathematical modeling of the PMSM was obtained and it is used as the load. Using the Hall sensor signal generation, the rotor position was calculated. The current loop and speed loop were developed to control the torque and voltage of the motor.

Proposed Simulink Model

• Hall signal generation: Three Hall signals from a pulse generator are produced with a phase shift of 120°, and at every 60° at least one Hall signal changes its state. The Hall signal MATLAB model is shown in Figure 1.

  Figure 1. Hall signal MATLAB model.

  Figure 1. Hall signal MATLAB model.
• Inverter: A DC bus of 36 volts is connected to three half-bridge inverters, which includes MOSFETs as switching devices. Three leg shunt resistors have to sense phase currents, as shown in Figure 2. The mathematically modeled PMSM is used as the load.

  Figure 2. Inverter with PMSM.

  Figure 2. Inverter with PMSM.
• Motor phase current into DQ frame: Motor phase currents are sensed with three leg shunt resistors. These three phase currents i.e., Ia, Ib, and Ic are modified to direct (Id) and quadrature (Iq) currents using Clarke and Park transformations, as shown in Figure 3.

  Figure 3. Phase currents into Id and Iq.

  Figure 3. Phase currents into Id and Iq.
• Current loop: The current loop contains two loops i.e., Id and Iq loops, as shown in Figure 4. Here, the command for Id is set to zero in order to obtain the highest torque i.e., by sustaining 90° between the stator and rotor flux. Kp and Ki of the current loop hold on the bandwidth and electrical time constant (${\tau }_e$).

$${\tau }_e=L/R,$$

(1)

where L is the phase inductance, H; R is the phase resistance, Ω.

Figure 4. Current loop.

Figure 4. Current loop.

The outcome of the current loop is Vd and Vq references, which are fed into the inverse Park and inverse Clarke transformations.

• Speed loop: A single PI block is used in the speed loop, where the outcome is the iq command for the current loop, as shown in Figure 5. The Kp and Ki values for the speed loop hold on the bandwidth and mechanical time constant (${\tau }_m$).

$${\tau }_m=J/B,$$

(2)

where J is the inertia of the motor, kg.m²; B is the frictional co-efficient.

Figure 5. Speed loop.

Figure 5. Speed loop.

• PWM generation: Three phase quantities using the inverse Park transformations are acquired from the Vd and Vq reference of the current loop and given to the space vector block to produce the 3rd harmonic sinusoidal wave, as shown in Figure 6.

  Figure 6. PWM generation.

  Figure 6. PWM generation.

For generation of the 3rd harmonic sinusoidal wave, the following equation is used:

$${\mathrm{V}}_{{\mathrm{a}}_{\_\mathrm{out}}}={\displaystyle \frac{2}{\sqrt{3}}} \left[{\mathrm{V}}_{\mathrm{a}}-{\displaystyle \frac{1}{2}}\left[\mathrm{Max}\left({\mathrm{V}}_{\mathrm{a},}{\mathrm{V}}_{\mathrm{b},}{\mathrm{V}}_{\mathrm{c}}\right)+\mathrm{Min} \left({\mathrm{V}}_{\mathrm{a}},{\mathrm{V}}_{\mathrm{b},}{\mathrm{V}}_{\mathrm{c}}\right)\right]\right];$$

(3)

$${\mathrm{V}}_{\mathrm{b}\_\mathrm{out}}={\displaystyle \frac{2}{\sqrt{3}}} \left[{\mathrm{V}}_{\mathrm{b}}-{\displaystyle \frac{1}{2}}\left[\mathrm{Max}\left({\mathrm{V}}_{\mathrm{a},}{\mathrm{V}}_{\mathrm{b},}{\mathrm{V}}_{\mathrm{c}}\right)+\mathrm{Min} \left({\mathrm{V}}_{\mathrm{a},}{\mathrm{V}}_{\mathrm{b},}{\mathrm{V}}_{\mathrm{c}}\right)\right]\right];$$

(4)

$${\mathrm{V}}_{\mathrm{c}\_\mathrm{out}}={\displaystyle \frac{2}{\sqrt{3}}} \left[{\mathrm{V}}_{\mathrm{c}}-{\displaystyle \frac{1}{2}} \left[\mathrm{Max} \left({\mathrm{V}}_{\mathrm{a},}{\mathrm{V}}_{\mathrm{b},}{\mathrm{V}}_{\mathrm{c}}\right)+\mathrm{Min} \left({\mathrm{V}}_{\mathrm{a},}{\mathrm{V}}_{\mathrm{b},}{\mathrm{V}}_{\mathrm{c}}\right)\right]\right].$$

(5)

The required gate signals for the MOSFET are achieved when the outcome of the space vector modulation block is differentiated with the triangle wave of 10 KHz.

• SVPWM: The generation of three phase voltages from the transformation block is fed into the SVPWM block, to obtain three space vector modification voltages, as shown in Figure 7.

  Figure 7. Open loop block diagram.

  Figure 7. Open loop block diagram.

2.2. Tuning of Motors

The software tuning is basically performed using codes. Nonetheless, we need to know how the basic code came into existence; therefore, an understanding of MCE Wizard parameters, a few Register Parameters, and tweaks in the programming must be studied.

• Motor Back-EMF constant (K_e): If given a motor whose Ke value is not known, or if the value given in the datasheet is not operating, then this can be obtained using the Back-EMF Test. Once the readings are obtained, further work is required. According to the test, we obtain numerous readings of the Back-EMF voltage and speed (at which the voltage is measured). In MCE Designer, the Back-EMF constant is described as V(ln-rms)/ krpm. The measured EMF will be between lines (RMS), but in MCE Designer it is described as line-neutral (RMS). Hence, using this equation would transform the measured value in MCE Designer into a suitable value. Units are in V(ln-rms)/krpm.

  $$\mathrm{Ke} \left(\mathrm{MCE} \mathrm{Designer}\right)={\displaystyle \frac{\mathrm{Measure} \mathrm{BEMF} \mathrm{Voltage} \left(\mathrm{V}\left(\mathrm{Line}-\mathrm{Line}\right)\right)}{\mathrm{Speed} (\mathrm{in} \mathrm{RPM} \mathrm{at} \mathrm{which} \mathrm{Voltage} \mathrm{is} \mathrm{measured})}} \times {\displaystyle \frac{1000}{\sqrt{3}}}.$$

  (6)
• Motor torque constant, K_t can be approximated from the Ke (BEMF constant).

$${ \mathrm{K}}_{\mathrm{t}}={\displaystyle \frac{9{ \ast \mathrm{K}}_{\mathrm{e}}}{100 \ast \pi }},$$

(7)

where ${ \mathrm{K}}_{\mathrm{e}}$ is in V(ln-rms)/krpm and ${ \mathrm{K}}_{\mathrm{t}}$ is in N-m/A(RMS).

• R_ph (phase resistance/motor resistance)

This can be verified through the R-L test, although this test only studies the resistance of the motor; the resistance of the additional cables connected to it is not considered. This can cause problems in the field, as the cables would lead to a change in the resistance value as the inverter sees it. If the cables are changed suddenly due to necessity, then the resistance would also be converted accordingly in the code.

• L_ph (phase inductance/motor Ld and Lq inductance)

This can be approximated by the R-L test of the motor. Normally, the inductance of the motor does not change by adding extra cable in the series of the motor.

• Poles

The poles are constant and can be counted in the motor if not given by the maker or any other source.

• Rated current/allowed current

The rated current is introduced. If the rated current is not sustained or increased beyond the restraint level, then it can lead to saturation or heating due to excess losses of i²R.

• Inertia of the load/motor

The inertia of the load can be approximated if the following are studied:

$$\begin{gathered} \mathrm{J}=[ {\displaystyle \frac{1}{2}} \times 2 \times {\mathrm{W}}_{\mathrm{Motor}} \times {{\mathrm{R}}^2}_{\mathrm{Motor}}]+[{\mathrm{W}}_{\mathrm{Blade}}\times \mathit{No}. \mathit{of} \mathit{blades} \times \{{\displaystyle \frac{{{\mathrm{R}}^2}_{\mathrm{Blade}}}{3}}+{{\mathrm{R}}^2}_{\mathrm{Motor}}+ \\ ({\mathrm{R}}_{\mathrm{Motor}}\times {\mathrm{R}}_{\mathrm{Blade}})\}], \end{gathered}$$

(8)

where ${\mathrm{W}}_{\mathrm{Motor}}$—motor weight, kg; ${\mathrm{R}}_{\mathrm{Motor}}$—motor radius, m; ${\mathrm{R}}_{\mathrm{Blade}}$—blade radius, m (single blade length, with all the attachments); ${\mathrm{W}}_{\mathrm{Blade}}$—blade weight, kg (single blade weight, with all the attachments).

After obtaining this formula, we can obtain the moment of inertia of the system (J).

After performing all the tests and obtaining the values, all the constants can be entered in Mc Wizard and the .h file created, so that the code can be generated and used in the program. The parameters are presented in

Table 1. Mc Wizard parameters.

S. No.	Motor Parameters	Values
1	Motor Model name	2.0 (22 mm)
2	Motor Rated Amps	3 A
3	Motor Poles	22
4	Motor Stator Resistance	4.47 HMS/phase
5	Motor Lq Inductance	64 mH
6	Motor Ld Inductance	64 mH
7	Motor Back-EMF Constant (Ke)	632.19 V/krpm
8	Motor Torque Constant (Kt)	10 N-m/A
9	Motor Shaft Inertia	3 Kg m^2

.

By collecting all the required values from Mc Wizard, and creating the .h file so that the code can be generated, dumped, and examined, the motor can be tuned.

• Hardware Development

This section holds the schematics of rotor position sense, inverter, battery voltage sense, and driver circuit.

• Hall Sensor Schematics

In PMSM motor control application, it is useful to precise detect the presence of the magnetic field in sequence to initiate the instantaneous rotor position. The engine is provided with Hall sensors; a Hall sensor is a transducer that changes the voltage in accordance to the attractive field. Three Hall sensors are fabricated in the stator at an angle of 120° to perceive the rotor position. The outcomes of the Hall sensors are linked to the GPIO pins of the controller, which produces the switching sequence as per commutation. The Hall sensor output pin is an open collector and hence has to be pulled up. The Hall sensor functions at 5 V; using a voltage divider, this it is converted into 3.3 V and fed to the microcontroller to sense the position of the motor. The Hall signal circuit is shown in Figure 8.

• Battery Voltage Monitoring

The bus voltage at 36 V is observed using a potential divider network such that the voltage across a 27 k resistor is 3.3 V. MCU_Bus Voltage signal is given to the ADC of the controller, as presented in Figure 9.

• Decoupling Capacitors

The decoupling capacitors are required to be connected closer to the microcontroller. These capacitors are added to the analog and digital supply pins for filtering purposes. The decoupling capacitor schematic is shown in Figure 10.

• MOSFET Selected

The MOSFETs chosen for the application have specifications of 60 V, 300 A, and 429 W, ensuring compatibility with the 36 V automotive standard. Parameters like DC bus voltage, power topology, and frequency of operation guide MOSFET selection. MOSFETs are voltage-controlled devices with positive temperature coefficients and high avalanche abilities, enabling efficient high-frequency switching with low switching losses and conduction losses. The gate-source voltage (VGS) in the datasheet determines the onset of drain current flow, with CGS charging influencing gate voltage and drain current behavior.

• Microcontroller selection

The selection of a 32-bit DSP controller is required for implementing complex algorithms like FOC with space vector modulation, necessitating a precise execution speed for 20 kHz PWM generation. On-chip ADC capacity is crucial for accurate voltage and current estimation, with considerations for sampling and quantization errors addressed through noise reduction techniques like low-pass filters and bypass capacitors. A higher bit converter is recommended to achieve the desired precision, while a fast conversion rate enhances closed-loop performance and allows for oversampling and filtering of noisy motor feedback signals.

3. Results and Discussion

3.1. Simulation Hall Sensor Waveform

The Hall sensor output shown in Figure 11 produces a unique pattern for regulating the rotation and relationship of the motor. In Simulink, similar Hall sensor patterns are initiated for the purpose of simulation. The frequency of the Hall sensor output is 100 Hz.

3.2. Ramp Generation from 0° to 360° and Direction of the Motor

The position of the rotor is approximated using the Hall sensor output i.e., ramp varying from 0° to 360°, as shown in Figure 12. For evaluating the direction of the motor, output 1 = clockwise rotation and 0 = anticlockwise rotation.

3.3. Outcomes Taken from the Motor Control Algorithm

• Hall Sensor Signals

Hall sensor signals of two phases at a moment in the clockwise direction are shown in the Figure 13, Figure 14 and Figure 15.

Figure 11, Figure 12, Figure 13, Figure 14 and Figure 15 display the essential Hall sensor waveforms vital for accurate commutation and control of the PMSM motor’s speed and position. These figures depict simulated and experimental Hall sensor signals, generating a distinctive pattern indicative of the rotor’s position. This pattern is a basis for developing the inverter’s switching sequence, facilitating motor drive. Incorporating these waveforms confirms the effective sensing and processing of rotor position feedback, ensuring precise motor operation.

• Back-EMF test result

The waveforms and average Ke graphs of the Back-EMF of the motor at numerous speeds are presented in Figure 16, Figure 17, Figure 18 and Figure 19. To find the Back-EMF, the motor is rotated at a constant speed and voltage at every phase for analysis of the power of the motor.

(a)

At 200 RPM

At 200 RPM, the Back-EMF RMS voltage obtained is 124 V.

(b)

At 150 RPM

At 150 RPM, the Back-EMF RMS voltage obtained is 100 V.

(c)

At 100 RPM

At 100 RPM the Back-EMF RMS voltage obtained is 67.1 V.

(d)

At 50 RPM

At 50 RPM the Back-EMF RMS voltage obtained is 32.8 V. With an increase in speed, the Back-EMF RMS voltage rises.

Here, with the iteration of a 10 rpm rise, from 10 rpm to 500 rpm, we recorded the Back-EMF results per phase, as shown in the graphs in Figure 20, Figure 21 and Figure 22.

(e)

Back-EMF at various rpm values (Phase AB)

Average of Phase AB (Ke) (L-N) = 372.6 V; average of Phase AB (Ke) (L-L) = 645.3 V.

(f)

Back-EMF at various rpm values (Phase BC)

Average of Phase BC (Ke) (L-N) = 374.9 V; average of Phase BC (Ke) (L-L) = 649.4 V.

(g)

Back-EMF at various rpm values (Phase AC)

Average of Phase AB (Ke) (L-N) = 344 V; average of Phase AB (Ke) (L-L) = 595.8 V.

Figure 16, Figure 17, Figure 18, Figure 19, Figure 20, Figure 21 and Figure 22 present the results of the back-EMF tests, which are crucial for determining the back-EMF constant (Ke), a significant parameter in PMSM motor control and modeling. These figures illustrate the measured back-EMF voltages across various speeds for each phase winding. Through analysis of these measurements, average Ke values are derived, which are essential for fine-tuning the motor control algorithm and estimating torque output. The inclusion of these findings showcases the experimental methodology employed to accurately ascertain the back-EMF constant, aiding in precise motor control.

• VFD drive with a three-phase PMSM motor

After the analysis of the VFD drive system learning, we obtained the tuning parameters, which are suitable for an application with a PMSM motor.

Table 2. VFD tuning motor parameters.

Parameters	Unit
Rated Power	1.5 kW
Drive Input Voltage	415 Volts
Drive Frequency	50 Hz
Motor Frequency	55 Hz
Rated RMS Current	3 Amperes
Number of Motor Poles	22
Back-EMF Type	Sinusoidal
Stator Resistance Rs	4.47 Ohms
d-axis Field Resistance Ld	64 mH
q-axis Field Resistance Lq	64 mH
Back-EMF Voltage (Mechanical) Es()	632.19 V(line-line-RMS/kRPM)
Torque Constant	10.47 Nm/Arms
Frequency Reference Upper Limit	55 Hz
Frequency Reference Lower Limit	1 Hz
Carrier Frequency Selection; kHz	5 KHz
0. To edit parameters, 1. To write protect	1
To show rpm screen first.	2
Control Type: 0. V/F Control, 1. V/F + Encoder Control, 2. Space Vector Control	2
Load Selection: 0. Normal Load, 1. Heavy Load	1
Source of frequency (rpm) command (Auto): 7. Digital Keypad Dial (Potentiometer)	7
Source of the frequency (rpm) command (HAND): 0. Digital Keypad	0
Operation Command (HAND): 0. Digital Keypad Keys, 1. External Switches	1
Digital Keypad Stop Key: 0. Stop key disable, 1. Stop key enable	1
Start-up frequency	0.50 Hz
Deceleration time	40 s
Auto acceleration/deceleration setting: 1. Auto acceleration, linear deceleration	1
Motor selection: 0. Induction motor, 1. SPM, 2. IPM	1
Rated speed of PMSM	350 rpm
Torque compensation gain	5
Proportional gain (P)	1.0 s
Integral time (I)	1.0 s
PWM mode selection: 2. Space Vector	2
Application selection: 0. Disabled, 1. User Parameter, 2. Compressor, 3. Fan, 4. Pump	3

and

Table 3. Performance report of PMSM motor.

S.N.	Motor Parameter	Initial Reading	Final Reading
1	Motor Shell Temperature (Thermal Gun)	35 °C	45 °C
2	Power Factor	0.52	0.49
3	Motor RPM	350	350
4	Power (VA)	953	1088
5	Power (W)	522~525	526~530
6	Motor Voltage (V)	384~391	388~409
7	Motor Current (A)	1.80~2.01	1.95
8	Input Delta VFD Voltage (Line-Neutral)	253	259
9	Input Delta VFD Voltage (Line-Line)	435	444
10	Input Current	1.46~1.59	1.50~1.53

detail the tuning parameters applied to the variable-frequency drive (VFD) during PMSM motor control. Table 2 outlines crucial parameters, such as rated values, control mode, and PWM settings, to ensure stable and efficient VFD operation. On the other hand, Table 3 presents the initial and final performance metrics following the testing of the tuned VFD with the PMSM motor. These metrics encompass motor temperature, power factor, speed, currents, and voltages. The inclusion of these tables serves to validate the tuning procedure undertaken and showcases the attained performance levels.

In 4 h of final testing on the PMSM motor with a 6 ft blade size and six blades, forward rotation of the motor and reverse rotation of the motor was conducted for the duration of 2 h each. A thermal gun was used to measure the motor temperature before the test begins and after the final completion of the test.

4. Conclusions

In this study, we presented a novel and practical approach to controlling permanent-magnet synchronous motors (PMSMs) using field-oriented control (FOC) with a focus on detailed hardware implementation and performance evaluation, an area not extensively covered in the existing literature. Our contributions include integrating advanced control techniques with practical hardware implementations, utilizing Hall sensors for precise speed and position control, developing and validating hardware circuitry, and implementing FOC with space vector modulation in MATLAB/Simulink. The use of the Embedded Coder toolbox in Simulink for efficient C code generation for DSP controllers marks a significant advancement, demonstrating practicality and efficiency in real-world applications. Our results indicate that the proposed control strategy offers improved performance in terms of torque and speed regulation, efficiency, and stability of the PMSM drive system. The experimental validation on a Texas Instruments microcontroller with a surface-mounted PMSM underscores our methodology’s effectiveness. The novelty of this study lies in its holistic approach, combining theoretical advancements with practical hardware implementations, bridging the gap between simulation and real-world applications, and providing a practical framework adaptable to various industrial applications. The findings from this research can be utilized in several ways: enhancing performance and reducing energy consumption in industrial automation; improving motor performance, battery life, and reliability in electric vehicles; offering precise control and reduced noise for aerospace and robotics applications; and serving as a valuable resource for academic researchers and educators. Future research directions will include integrating advanced sensorless control techniques to reduce hardware reliance; applying machine learning algorithms for adaptive control and fault diagnosis to enable real-time optimization and predictive maintenance; expanding hardware implementation to other motor types and power electronics to validate versatility; and investigating environmental factors such as temperature variations and electromagnetic interference to ensure reliability in diverse conditions. These future studies will solidify PMSM control systems’ practical applicability and performance enhancements in various industrial and technological fields.