Advanced direct torque control based on neural tree controllers for induction motor drives.

This paper introduces a novel direct torque control approach based on the decision tree (T-DTC), employing artificial neural networks that are effectively trained to enhance accuracy and robustness. The main objective of T-DTC is the substantial reduction of flux and torque ripples inherent in the conventional DTC, ensuring effective control of the induction motor. The conventional hysteresis controllers for stator flux and electromagnetic torque are replaced by two advanced controllers named M5 Prime model trees. Additionally, the traditional switching table is substituted with a novel decision tree table utilizing the classifier algorithm 4.5. The effectiveness of the proposed T-DTC strategy is demonstrated through simulation in MATLAB/Simulink and validated in real-time using an HIL platform based on OPAL-RT OP 5600 and Virtex 6 FPGA ML605. The results obtained demonstrate a notable improvement compared to existing techniques in the literature. [Display omitted] • An enhanced DTC strategy for induction motor based on decision tree is proposed. • A neural learning process is applied for the designed controllers. • Easy real-time implementation of the Tree-DTC strategy via the HIL tests is realized. • Considerable minimization of stator flux and electromagnetic torque ripples is achieved. • Robust Tree-DTC strategy in terms of speed monitoring and disturbance rejection is achieved. [ABSTRACT FROM AUTHOR]