Conventional scalar voltage–frequency (V/f) control is widely adopted in industrial drives due to its simplicity and low cost; however, at very low operating frequencies, it often suffers from poor transients, speed oscillations, and excessive start-up currents when a proportional–integral controller (PIC) is used in the speed loop. These limitations hinder stable operation and reliable start-up under load, motivating the need for a more robust low-speed control scheme. This paper presents the design and experimental validation of an improved V/f scalar control based on a Mamdani fuzzy logic controller tuned by particle swarm optimization (PSO). A physical prototype was implemented using speed and current sensing and LN CO3636-3B that enables the control strategies, updates K_p and K_i through Mamdani inference in a discrete PI law, and drives a self-commutated inverter via PWM. Experiments were performed under variable conditions, demonstrating that the proposed controller at 6.06% of rated speed achieves stable regulation with M_p = 10% and t_s = 0.445 s, whereas the PIC exhibits instability with steady oscillations and M_p up to 44%. The proposed controller keeps the inrush current within the design limit below 1.615 A. Overall, the PSO-tuned Mamdani fuzzy controller improves low-speed robustness while respecting transient design constraints.

Efforts have continually been directed toward optimizing processes in various fields, and the application in induction motors is no exception. Scalar control V/f offers a straightforward method to regulate the speed of a three-phase induction motor (TIM). However, it faces challenges at low speeds or proportionally at low frequencies, often failing to operate below 20% of its rated speed. This control typically pairs with a PI controller (PIC) for closed loop speed regulation, but its limited control range hinders performance at low speeds. Although intelligent methods have been developed to improve scalar V/f control, attention is often focused on high speeds, while control at low speeds is overlooked. This paper presents the simulation of a fuzzy controller (FC) with a Mamdani-type structure designed to achieve effective low-speed control of a TIM using the V/f scalar control technique. The results not only show improvements in overshoot and settling time but also reveal that the FC can control speeds as low as 6.06% of the rated speed, and it ensures a starting current below the designed motor current under load. Comparative analysis indicates that the FC outperforms the PIC in low-speed control, and it provides an optimal inrush current across different low speeds.

This article presents an online additive fault-detection system for the speed sensor of a 200 W shunt-type direct current (DC) motor, integrated into a power module controlled by an Insulated Gate Bipolar Transistor (IGBT). The system is designed to trigger an alarm signal when an additive fault occurs by comparing the Kalman Filter (KF) residual against a predefined detection threshold. Three specific fault types in the speed sensor were analyzed: offset, disconnection, and sinusoidal noise. Experimental results demonstrate effective fault detection across a speed range of 80 to 690 rpm under no-load conditions. However, when a constant torque of 0.5 Nm is applied, both the detection threshold and the subset of reliably identifiable faults must be adjusted. The main contribution of this study is the development of a customized real-time fault detection framework and the characterization of residual variations caused by unmodeled load disturbances in actual hardware. This approach improves the monitoring and fault-diagnosis capabilities of sensor systems in DC motors by quantifying the stochastic behavior of residuals under different operating constraints.