Comparative Study of Electromechanical Models for Crouzet DC Motor and Mitsubishi Single-Phase AC Motor Using System Identification Methods

Abstract

This study presents a comparative analysis of the electromechanical models for the Crouzet DC Motor 82800502 and the Mitsubishi Electric SC-QR 1/2 HP single-phase AC motor using system identification methods. The research focuses on developing mathematical models for both motors, employing Laplace transforms, differential equations, and transfer functions to characterize their dynamic behaviors. The Crouzet DC motor, with its linear and direct current control, is modeled using electrical and mechanical equations, highlighting parameters such as armature resistance (3.9 Ω), inductance (9.35 mH), and torque constant (0.0627 Nm/A). The Mitsubishi AC motor, a capacitor-start induction motor, is analyzed through dq-axis transformations, incorporating stator resistance (4 Ω), inductance (162 mH), and slip-dependent torque characteristics.

System identification techniques, including experimental, analytical, and parameter estimation methods, are applied to derive accurate models for both motors. The DC motor's transient response, with a mechanical time constant of 15 ms, demonstrates faster dynamics compared to the AC motor, which exhibits slower transient behavior due to its starting capacitor and rotational inertia. Steady-state analysis reveals that the AC motor achieves higher efficiency (70%) under nominal conditions, while the DC motor operates at 54% efficiency. Stability assessments, conducted through Laplace domain analysis and MATLAB simulations, confirm the DC motor's superior stability in precision applications, whereas the AC motor requires careful consideration of starting transients.

The study also explores block diagram reduction techniques to simplify the models for controller design, such as PID and adaptive control strategies. Practical implications for microcontroller-based implementations are discussed, emphasizing the trade-offs between dynamic response, efficiency, and control complexity. The findings provide valuable insights for selecting and optimizing motor systems in industrial and household applications, based on performance requirements and operational constraints.