Fuzzy Direct Torque Control Application-Specific Integrated Circuit with Neural Network and Fuzzy Hysteresis Controller for Induction Motor

This study proposes a direct torque control (DTC) application-specific integrated circuit (ASIC) equipped with a neural network and a fuzzy hysteresis controller to achieve seamless control of a three-phase induction motor. In the proposed DTC system, feedback currents and voltages measured at the stator are fed into the hysteresis controller and a switching table. Subsequently, six-arm voltages are generated based on the voltage vector selector table to drive the RM5G inverter. However, severe switching noise is present in the power transistors of the inverter. These problems lead to numerous large ripples, instability, and delayed torque and flux responses at the stator. To address the aforementioned challenges, this study proposes a fuzzy controller to enhance flux signals. This controller incorporates a fuzzifier, a fuzzy rule base, and a defuzzifier. Additionally, a backpropagation neural network control is employed to improve torque signals. The multilayer neural network is utilized not only to calculate torque rapidly but also to enhance calculation accuracy. The proposed control method effectively reduces flux and torque errors, facilitating smooth control of the three-phase induction motor. After functional verification on an FPGA board, the proposed design is implemented on an ASIC fabricated using the TSMC <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.18-\mu \mathrm{m}$</tex> CMOS process. The results indicate a chip area of approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.959\times 0.9584\text{mm}^{2}$</tex> and a power consumption of 2.2524 mW at a supply voltage of 1.8 V and an operating frequency of 10 MHz.