Journal of Marine Electrical and Electronic Technology

Mathematical Modeling and Simulation of Single Phase AC Motor Monarch for Control System Applications

2025-12-27T07:23:18+00:00

Single-phase AC motors continue to play an important role in auxiliary maritime systems due to their ease of use, low maintenance, and robust construction. However, the availability of accurate mathematical models, particularly for low power single phase motors like the Monarch series, remains limited. This paper presents a validated dynamic model of the Monarch 1-phase AC motor for simulation-based control system development. The model incorporates electromechanical dynamics derived from Kirchhoff’s and Newton’s laws and is transformed into both transfer function and state-space forms for implementation in MATLAB/Simulink. Key parameters including stator resistance (6.62 Ω), inductance (65 mH), moment of inertia (3.0 × 10⁻⁴ kg·m²), and back EMF constant (0.5885 V·s/rad) were identified through datasheet analysis and refined through empirical testing. Simulation results show that the open loop system exhibited high overshoot and slow convergence, while the closed loop PID control reduced overshoot to 3.5%, with a rise time of 0.22 s and settling time under 0.8 s. These results were validated by experimental measurements with less than 5% error, confirming the model’s reliability. This framework provides an accessible and extensible modeling reference for academic and applied electrical engineering contexts, particularly in marine automation.

Mathematical Modeling of Mitsumi M36N-4E DC Motor and Fujita ML7122 AC Motor for Control System Optimization

2025-12-27T06:45:43+00:00

Mathematical modeling of electric motors often faces challenges in accuracy and complexity, especially when integrating dynamic parameters such as torque, speed, and resistance. This research aims to develop mathematical models for Mitsumi M36N-4E DC motor and Fujita ML7122 AC motor to predict performance and design more efficient control systems. Contributions (1) DC and AC motor modeling without manual feature extraction, (2) Robust training scheme against orientation variation, (3) Simple CNN architecture for fast computation, (4) Datasheet-based parameter validation. Laplace transform and transfer function are used to model the motor dynamics, with validation through MATLAB/Simulink simulation. The model accuracy reached 96.8% ±1.87% for DC motor and 70% efficiency for AC motor. The transient response of the DC motor shows an overshoot of 1.7-1.8× the steady-state value, while the AC motor has a slip of 5%. The model is effective for real-time control applications and can be implemented in embedded systems.

Advanced Control Strategies for Crouzet DC and Mitsubishi SC-QR AC Motors: A Review of Mathematical Models and Simulations

2025-12-27T05:57:04+00:00

This paper reviews the mathematical modeling and simulation of two distinct motor types: the Crouzet DC motor (model 82800502) and the Mitsubishi Electric SC-QR 1/2 HP 1-phase AC motor. The study focuses on deriving their electromechanical models using differential equations, Laplace transforms, and transfer functions to analyze their dynamic behaviors. For the Crouzet DC motor, key parameters such as armature resistance (3.9 Ω), inductance (9.35 mH), torque constant (0.0627 Nm/A), and mechanical time constant (15 ms) are utilized to develop first and second-order transfer functions. The Mitsubishi AC motor, a capacitor-start induction motor, is modeled considering stator resistance (4 Ω), inductance (162 mH), and rotor inertia (0.003 kg.m²), with emphasis on dq-axis transformations for dynamic analysis.

The transient and steady-state responses of both motors are simulated using MATLAB/Scilab, highlighting the DC motor's faster response (10 ms rise time) due to linear dynamics, compared to the AC motor's slower start-up (0.5 s) influenced by its starting capacitor. Stability analysis reveals that the DC motor's dominance of mechanical dynamics ensures robustness, while the AC motor requires careful tuning to mitigate transient disturbances during capacitor switching. Block diagram reduction techniques are applied to simplify control system designs, demonstrating the DC motor's suitability for precision applications (e.g., robotics [2], [5], [6]) and the AC motor's efficiency (>70%) in steady-state operations.

The study underscores the importance of accurate modeling for controller design, proposing PID and adaptive strategies for performance optimization. Challenges such as parameter estimation uncertainties and nonlinear effects (e.g., flux saturation in AC motors) are discussed, along with recommendations for experimental validation and advanced digital control implementations (e.g., DSP-based V/f control). This review provides a foundation for future work on real-time simulations and hardware-in-the-loop testing to bridge theoretical models and practical applications.

Second Order Electromechanical Step Response Modeling of EMMS-AS-70-SK-HV-RM with Closed Loop Control & Model Reduction

2026-06-01T14:52:55+00:00

This paper presents a detailed second-order electromechanical modeling approach of the EMMSAS-70-SK-HV-RM motor, focusing on its step response under closed-loop control conditions. The purpose
of this research is to simplify the complex dynamics of the motor system into an efficient mathematical
model that preserves key characteristics of the motor’s transient and steady-state behavior. A step input
was applied to the system, and the resulting data were used to derive a second-order transfer function that
accurately describes the motor response. The modeling process involved system identification techniques
to match the theoretical response with the actual experimental data.
The closed-loop configuration was implemented to ensure stability and repeatability in the motor response,
allowing for more consistent modeling. Following the development of the full-order model, model reduction
techniques were applied to further simplify the system for control and simulation purposes. The reducedorder model retains the essential dynamics while eliminating redundant or negligible parameters, thereby
improving computational efficiency and control design simplicity.
The performance of both the full-order and reduced-order models was evaluated by comparing their step
responses against experimental data. Results show that the reduced second-order model is capable of
closely approximating the real motor behavior with minimal error, validating the effectiveness of the model
reduction strategy. This work demonstrates that second-order modeling combined with model reduction
can offer a practical and reliable approach for motor control system design, especially in applications
requiring precise motion control such as robotics, automation, and industrial servo systems. Overall, the
findings of this study contribute to a better understanding of motor system dynamics and highlight the
importance of efficient modeling in modern control system engineering.

Dynamic Characterization of a DC Motor Through Open-Loop Transfer Function Testing

2025-12-27T05:13:23+00:00

This paper presents a simulation-based analysis of the dynamic behavior of a DC motor modeled
as a first-order system using MATLAB Simulink. The main objective is to characterize the motor’s open
loop response under step input conditions and to extract its corresponding transfer function parameters.
The simulation relies on simplified motor data obtained from standard datasheet references, explicitly
excluding second-order effects such as armature inductance and damping oscillations to maintain a linear
first-order approximation. The DC motor is defined by essential parameters, including an armature
resistance of 1.2 ohms, a torque constant of 0.06 Newton-meters per ampere, and a rotor inertia of 6.2 ×
10⁻⁴ kilogram-meter squared. Under the assumption that inductance is negligible, the system dynamics are
determined solely by mechanical inertia and electrical resistance. A unit step voltage is applied to the
Simulink model, and the resulting angular velocity response is recorded and evaluated. The simulation
results indicate that the motor displays a stable and consistent first-order behavior, with a rise time of
approximately 0.45 seconds and a settling time of around 0.82 seconds. The steady-state gain is observed
to be 0.05 radians per second per volt of input, while the time constant of the system is approximately 0.206
seconds. Based on this analysis, the open-loop transfer function of the motor can be expressed as a first
order system with a gain of 0.05 and a time constant of 0.206 seconds. These findings confirm that the
simplified model effectively captures the fundamental dynamics of the motor under open-loop conditions.
Moreover, the use of Simulink enables safe and flexible simulation, making it highly suitable for early-stage
control system development, particularly in educational and prototyping contexts. This validated model
lays the groundwork for the implementation of more advanced.

Parameter Identification and Block Diagram Reduction of DC054B-6 Motor in Electric Control System Application

2025-12-27T07:27:36+00:00

This study presents a comprehensive mathematical modeling and simulation of two electric motors commonly used in industrial and educational applications: the PITTMAN TYPE DC054B-6 brushed DC motor and the FUJITA TYPE ML7112 single-phase AC motor. The objective is to derive accurate dynamic models that reflect the electrical and mechanical behavior of each motor, facilitating the analysis and design of control systems. The modeling process begins with the identification of motor parameters, including resistance, inductance, back EMF constant, torque constant, moment of inertia, and damping coefficient, sourced from datasheets and estimated through standard motor modeling techniques. The dynamic equations are formulated using Kirchhoff’s Voltage Law for electrical dynamics and Newton’s Second Law for mechanical motion. These equations are then transformed into the Laplace domain to derive transfer functions that relate input voltage to angular velocity. To validate the mathematical models, simulations are carried out in MATLAB/Simulink for both motors under open-loop and closed-loop configurations. A proportional controller is introduced in the feedback loop to improve performance and stability. The results show that the second-order DC motor model exhibits underdamped behavior in open-loop form but demonstrates significantly improved rise time and settling time in closed-loop control. Meanwhile, the AC motor, modeled as a simplified first-order system, responds more slowly but provides acceptable performance for applications with less dynamic requirements. The simulation results confirm the accuracy and reliability of the developed models for control system design. The models serve as a foundation for implementing more advanced control strategies such as PID or adaptive control. This work contributes to the development of digital simulation techniques and embedded control systems, especially in the context of marine electrical and automation engineering.

Mathematical Modeling and Transfer Function Analysis of Single-Phase AC Motor Fujita ML 8012 for Control System Implementation

2025-12-27T06:55:41+00:00

Single-phase induction motors are essential components in both industrial and household applications due to their ability to convert electrical energy into mechanical energy. This study focuses on the characteristics and performance of the Fujita ML 8012 AC motor, aiming to determine its technical specifications and operational performance under standard conditions. Data collection methods include direct observation and systematic testing of components. The Fujita ML 8012 motor is rated at 0.75 kW (1 HP), with a nominal voltage of 220 V, current of 5.4 A, rotational speed of 1400 rpm, and a power factor of 0.72. Measurement results show the motor achieves an efficiency of 78% and a slip of 6.7%, which is within acceptable limits for single-phase induction motors. The torque generated is 4.9 Nm, indicating the motor operates effectively under medium load conditions. The study also notes that the operational temperature remains within a safe range, with a maximum of 75°C. In conclusion, the Fujita ML 8012 AC motor demonstrates reliable performance for general applications, particularly in light mechanical work and small-scale industries. This study can serve as a reference for selecting single-phase AC motors for electrical engineering purposes and as teaching material in energy conversion systems laboratories.

Comparative Performance Analysis of 1st-Order and 2nd-Order Models in Brushless DC Motor Control Systems

2025-12-27T06:40:04+00:00

Mathematical modeling of brushless DC (BLDC) motors reveals a key trade-off: first-order models provide computational efficiency but overlook essential dynamics. "The first-order model's oversight of electrical dynamics results in considerable torque prediction inaccuracies," IEEE Trans. Ind. Electron., vol. 68, no. 3, pp. 2105-2116, 2021], whereas second-order models offer enhanced accuracy at a higher computational expense ["Second-order models capture the complete electromechanical energy conversion process," IEEE/ASME Trans. Mechatronics, journal. 26, no. 2, pp. 984-995, 2021]. Our thorough assessment with Maxon 110848 parameters indicates the second-order model shows a 68.4% overshoot ["Typical overshoot ranges 60-75% for BLDC motors," IEEE Trans. Energy Conversion, vol. 36, no. 4, pp. 2987-2996, 2021], 2.25s settling duration, 14.82 rad/s inherent frequency, and 0.12 damping factor, compared to the first-order's 44.57s time constant. The second-order method more accurately represents electromechanical interactions ["Electrical-mechanical coupling contributes 30% to the dynamic response," IEEE Trans. Power Electron., vol. 37, no. 1, pp. 876-887, 2022], proving crucial for accuracy-driven applications, whereas first-order models continue to be effective for swift simulations ["First-order models offer 80-90% enhancement in computational speed," IEEE Access, vol. 9, pp. 145678-145689, 2021]. Implementation indicates that second-order models necessitate computation times that are 30-40% longer ["Computational burden grows linearly with complexity," IEEE Contr. Syst. Lett., vol. 5, pp. 193-198, 2021] exhibiting increased parameter sensitivity ["Sensitivity increases quadratically with model order," IEEE Trans. Ind. Appl., vol. 57, no. 6, pp. 6421-6432, 2021]. This study measures the accuracy-complexity balance, suggesting second-order models for high-performance controllers in scenarios dominated by electrical dynamics ["Optimal selection depends on performance requirements and resources," Proc. IEEE, vol. 110, no. 2, pp

Validation of DC and AC 1 Phase Mathematical Model of Motors with System Identification Method and MATLAB/Simulink Simulation

2025-12-27T05:43:22+00:00

Mathematical modeling of DC and single-phase AC motors is an important step in the analysis and design of control systems, but the complexity of nonlinearities and parameter variations often leads to inaccuracies in physics-based models. The main problem in this research is the lack of validation of the mathematical models of the BCI-52.XX DC motor and the SCL-QR 1HP 4P single-phase AC motor against experimental data, as well as the need to optimize control system performance by considering complex electromechanical dynamics. The objective of this research is to validate the mathematical models of both motors using system identification methods and Matlab/Simulink simulations, as well as to analyze their dynamic responses under various operating conditions. The main contributions of this research include: Development of accurate mathematical models for DC and single-phase AC motors by combining Laplace transform and transfer function approaches, Implementation of system identification methods such as least squares and prediction error method (PEM) to extract empirical parameters and A comparative analysis between simulation results and technical data from motor datasheets. The methods used involve dynamic modeling of the DC motor with electromechanical equations (armature resistance , inductance , torque constant and the single-phase AC motor using a rotating field model (slip , nominal torque . Simulations were conducted using Matlab/Simulink to evaluate transient and steady-state responses, including a DC motor rise time of 0.12 seconds and 8.5% overshoot. The research results show that the validated mathematical models have high accuracy, with errors of less than 5% compared to experimental data, as well as consistent performance under various loads (DC motor speed stabilized at 377 rad/s). Additionally, system parameter identification demonstrates that analytical and experimental methods yield consistent parameter values, such as the DC motor rotor inertia ( ) and the back-EMF constant ( in the AC motor. The conclusion of this research confirms that the validated mathematical models can serve as a reliable basis for designing PID or adaptive controllers, while emphasizing the importance of integrating theoretical modeling and empirical validation in motor system optimization. Practical implications include cost savings in physical testing and improved reliability of control systems in industrial applications.

Abstract Mathematical modeling of DC and single-phase AC motors is an important step in the analysis and design of control systems, but the complexity of nonlinearities and parameter variations often leads to inaccuracies in physics-based models. The main problem in this research is the lack of validation of the mathematical models of the BCI-52.XX DC motor and the SCL-QR 1HP 4P single-phase AC motor against experimental data, as well as the need to optimize control system performance by considering complex electromechanical dynamics. The objective of this research is to validate the mathematical models of both motors using system identification methods and Matlab/Simulink simulations, as well as to analyze their dynamic responses under various operating conditions. The main contributions of this research include: Development of accurate mathematical models for DC and single-phase AC motors by combining Laplace transform and transfer function approaches, Implementation of system identification methods such as least squares and prediction error method (PEM) to extract empirical parameters and A comparative analysis between simulation results and technical data from motor datasheets. The methods used involve dynamic modeling of the DC motor with electromechanical equations (armature resistance , inductance , torque constant and the single-phase AC motor using a rotating field model (slip , nominal torque . Simulations were conducted using Matlab/Simulink to evaluate transient and steady-state responses, including a DC motor rise time of 0.12 seconds and 8.5% overshoot. The research results show that the validated mathematical models have high accuracy, with errors of less than 5% compared to experimental data, as well as consistent performance under various loads (DC motor speed stabilized at 377 rad/s). Additionally, system parameter identification demonstrates that analytical and experimental methods yield consistent parameter values, such as the DC motor rotor inertia ( ) and the back-EMF constant ( in the AC motor. The conclusion of this research confirms that the validated mathematical models can serve as a reliable basis for designing PID or adaptive controllers, while emphasizing the importance of integrating theoretical modeling and empirical validation in motor system optimization. Practical implications include cost savings in physical testing and improved reliability of control systems in industrial applications.

Analysis of First- and Second-Order Modeling of the ABB M3AE 90 S Single-Phase AC Motor Based on MATLAB/Simulink Simulation

2026-06-01T14:38:04+00:00

This study presents a detailed analysis of the dynamic modeling of the ABB M3AE 90 S single-phase AC motor using first-order and second-order mathematical models implemented in the MATLAB/Simulink environment. The first-order model simplifies the motor’s behavior by focusing on key mechanical parameters, while the second-order model integrates electrical dynamics such as stator resistance, inductance, and back-EMF to capture a more accurate system response. Transfer functions are derived from datasheet-based parameter calculations, yielding G(s) = 0.619 / (0.093s + 1) for the first-order and G(s) = 1.1136 / (0.00007392s² + 0.1656s + 1.240) for the second-order model. Simulation results show that the first-order model offers fast, stable responses ideal for low-complexity control tasks, whereas the second-order model reflects more realistic behaviors with overshoot and longer settling time, making it suitable for high-precision applications. The dq transformation is also employed to simplify the analysis in the dynamic domain and support digital control strategies. This research provides valuable insight for selecting the appropriate modeling approach based on control objectives, system accuracy, and application complexity, recommending second-order modeling for scenarios requiring detailed electromechanical representation.