This study presents an experimental comparison between Direct Torque Control (DTC) and Finite Control

Set–Predictive Torque Control (FCS-PTC) for a three-phase induction motor (IM) drive, emphasizing their

implications for energy efficiency and sustainable industrial operation. Both control methods aim to regulate

torque and stator flux yet differ in voltage-vector (VV) selection principles. DTC employs a fixed look-up

table with hysteresis controllers, while FCS-PTC evaluates all inverter states through a cost-function-based

prediction. Experimental implementation using a dSPACE DS1104 platform was carried out at three operating

speeds—286 r/min, 764 r/min, and 1432 r/min—to quantify torque and flux ripples using statistical analysis.

At low speed (286 r/min), FCS-PTC achieved a torque ripple of 0.0948 N·m and a flux ripple of 0.0072 Wb,

compared with DTC’s 0.2350 N·m and 0.0109 Wb. Similar improvements were observed at medium and high

engineering education and workforce training in advanced control methods. The findings demonstrate that

predictive torque control not only improves technical performance but also contributes to broader objectives of

sustainable industrial development and capacity building.

Efficiency; Sustainable Technology

Induction motors (IMs) are the backbone of modern industry. It drives nearly half of global electrical energy

consumption in applications ranging from manufacturing and transportation to healthcare and household

systems. Their widespread use highlights both their economic significance and their potential role in achieving

global energy sustainability. Enhancing the efficiency and stability of IM operation therefore holds strong

fast dynamic response [3]. Introduced in the 1980s, DTC eliminates the need for current regulators and pulse

width modulation by employing hysteresis comparators and a look-up table to determine the appropriate VV

based on instantaneous torque and flux errors [4]. Despite these advantages, DTC is often criticized for high

torque and flux ripple, variable switching frequency, and reduced performance near sector boundaries due to

limited vector selection options [5-8].

To address these limitations, Predictive Torque Control (PTC), a variant of Finite Control Set-Model

Predictive Control (FCS-MPC), has emerged as a promising alternative [9, 10]. PTC employs a predictive

model of the machine and evaluates a cost function at each sampling instant to determine the most suitable VV

[11]. Unlike DTC, PTC evaluates all possible switching states in a conventional two-level inverter, thereby

offering greater control flexibility and improved resolution [12-14].

DTC’s computational efficiency. The study highlighted PTC's flexibility in VV evaluation through cost

functions, while DTC maintained an advantage in computational simplicity. Krupa and Koraddi [22] applied

PTC in the context of electric vehicle applications, demonstrating that the approach can maintain accurate

torque control even under load disturbances and parameter variations using a simulation platform. Xu et al.

[23] introduced a hybrid control method combining principles from DTC and Model Predictive Flux Control to

reduce torque ripple in switched reluctance motors, with experimental validation confirming improvements in

ripple suppression and dynamic robustness.

These studies confirm the benefits of predictive control methods and intelligent VV selection. However, most

existing work focuses on performance indicators such as ripple reduction and transient response, with limited

emphasis on how VVs are selected and transitioned in real time. Karlovsky and Lettl [24] analyzed switching

training engineering students and practitioners in sustainable control design, supporting both academic

capacity building and industrial innovation. By linking technical advancement with societal goals, the research

contributes to the broader pursuit of cleaner, smarter, and more sustainable electrical drive systems.

To investigate and compare the VV selection mechanisms in DTC and FCS-PTC, a mathematical model of the

two-level voltage source inverter (VSI) fed IM is established in the stationary reference frame. This model

forms the theoretical foundation for analysing VV behaviour and is validated through experimental testing

using a dSPACE-controlled inverter-fed IM system.

The stator and rotor flux linkages are defined by:

where:

are the stator and rotor current vectors

are the stator and rotor resistances

is the rotor electrical angular speed

The mechanical dynamics of the motor are described by:

is the

load torque

enables the inverter to regulate torque and flux directly.

Fig 1. Voltage vector positions and stator flux orientation in the α–β plane

The effect of each VV depends on its orientation relative to the stator flux vector. Referring to Fig. 1, when the

act as reverse tangential

vectors, reducing torque or changing its direction depending on the control objective [25, 26]. The remaining

is estimated using the following relation in the stationary α–β reference frame:

appropriate VV to apply via a predefined look-up table (Table 2) [28, 29].

Table 2 Conventional Look-up Table (LUT)

the block diagram of the implemented FCS-PTC scheme.

Fig. 3 Block diagram of the Predictive Torque Control (PTC) scheme

The stator flux and torque estimations are based on the same expressions used in the DTC method (refer to

equations (11) and (14)). These estimates serve as inputs to the prediction model, which predicts the stator flux

at the next control interval using:

of the stator current vector

Once the predictions for all VVs are computed, a cost function is used to determine the most suitable VV. The

cost function employed in this work is given by:

medium, and high operating conditions: 286 r/min (20% of rated speed), 764 r/min (approximately 57% of

rated speed), and 1432 r/min (rated speed). No external mechanical load was connected to the motor shaft. The

motor operated solely against its internal frictional torque. This condition allows for a clearer assessment of

control performance without interference from load dynamics. Additionally, since the VV selection logic in

DTC and FCS-PTC is not influenced by load torque, the no-load condition provides a valid basis for

comparison. The complete laboratory setup is illustrated in Fig. 4.

Fig. 4 Experimental setup for real-time implementation of DTC and FCS-PTC using a two-level VSI and a

The experiments were carried out on a three-phase squirrel-cage IM (186 W, 1425 r/min, 50 Hz, 190 V, 1.4 A,

A) with 0.47 μF / 1600 V snubber capacitors for over-voltage protection. A dSPACE DS1104 controller board

switching frequency of 20 kHz. The DS1104 provides 12-bit/16-bit ADCs (±10 V range) for signal acquisition

and 16-bit DACs (±10 V range) for analogue outputs, ensuring accurate and reproducible control

Motor currents were measured with Hall-effect current sensors (±50 A range, powered by ±12.5 V supply)

calibrated via a short-circuit test. Each sensor employed five conductor turns on the magnetic core to improve

sensitivity, with a gain of 9 V/A, yielding reliable measurements within ±2% accuracy. Speed was measured

During each test, the electromagnetic torque, stator flux, and applied VV signals were monitored via the

DS1104 DAC channels and Hall-effect current sensors, routed to a Rohde & Schwarz digital oscilloscope

through shielded BNC cables to minimise noise. The oscilloscope traces were saved in CSV format and

transferred to a PC via USB. The files were then imported into MATLAB using the readtable function for

post-processing. In MATLAB, torque and flux ripples were quantified by calculating the standard deviation of

their time-domain waveforms. VV switching behaviour was extracted by threshold-based classification of the

DAC outputs corresponding to the applied VVs. The resulting discrete labels were subsequently counted over

analysis (e.g., standard deviation for ripple quantification). Direct oscilloscope measurement alone could not

ensure the same level of repeatability or allow synchronized comparison across operating points. Hence, the

extraction and MATLAB-based analysis ensured a fair and transparent evaluation of DTC and FCS-PTC

performance. The evaluation focused on three key performance indicators: torque ripple, stator flux variation,

and VV switching behaviour.

Prior to the comparative tests, both controllers were tuned to ensure a fair, near-optimal operating point for our

setup. For DTC, flux and torque hysteresis widths were swept within practical bounds reported in the literature

(flux: 0.005–0.02 Wb; torque: 0.1–0.3 N·m) [30, 31] and evaluated at 764 r/min (medium speed) to balance

flux/ torque ripple and switching activity. The selected ±0.01 Wb (flux) and ±0.2 N·m (torque) produced low

interference. Such conditions allow a fair assessment of the voltage-vector selection mechanism and its direct

impact on torque and flux regulation. Nonetheless, this configuration does not capture the dynamic responses

or efficiency variations that occur under real industrial load conditions. Future extensions of this study will