Design and Simulation of Induction Motor Speed Control Based on Artificial Neural Network (ANN) on Scrambler Ditrix Motorcycle

The rise in conventional motorcycle use (30% in the last decade) has led to environmental concerns. Electric motorbikes are seen as a solution, but their price remains a hurdle. This study focuses on optimizing electric vehicle motor and controller selection to address affordability. While induction motors are praised for their simplicity, durability, and affordability, controlling them is complex due to varying loads. Field Oriented Control (FOC) helps manage torque and flux, but speed regulation is still needed. This study proposes an Artificial Neural Network (ANN) combined with conventional Proportional-Integral-Derivative (PID) control for improved speed regulation in electric motorbikes. Simulation results show that the ANN-based PID controller (NNPD) achieves better speed response than the conventional PID controller, with a lower average steady state error (2.1125% vs 3.056%) for the Scrambler Ditrix motorcycle.