Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Ha, Vo Thanh
Unknown Affiliation
Control & Systems Engineering Electrical & Electronics Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics

 1 
Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot Ha, Vo Thanh; Thi Thuong, Than; Ngoc Truc, Le
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7847

Abstract

This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.
Torque control of PMSM motors using reinforcement learning agent algorithm for electric vehicle application Ha, Vo Thanh; Tuan, Duong Anh; Van, Tran Thuy
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7852

Abstract

As electric vehicles (EVs) demand higher performance and efficiency, precise torque control in interior permanent magnet synchronous motors (IPMSMs) becomes increasingly vital. This paper introduces a reinforcement learning (RL)-based method to optimize torque control in IPMSMs. The RL agent is trained to regulate d-axis and q-axis currents, producing stator voltages to follow the desired motor speed. The control system includes an observation vector, voltage-based actions, and a specially designed reward function. Due to the nonlinear dynamics of the motor, training the agent requires significant computational effort. MATLAB/Simulink simulations are performed to compare the RL controller with a traditional PI controller. Results indicate that the RL controller delivers quicker and more accurate performance, although additional training is necessary to minimize overshoot.
Co-Authors Ha, Vo Thu Lam, Nguyen Tung Ngoc Truc, Le Thi Thuong, Than Truc, Le Ngoc Tuan, Duong Anh Van, Tran Thuy Vinh, Vo Quang