Hong-Vinh Vu

I'm an undergraduate at Hanoi University of Science and Technology (HUST), majoring in Control and Automation Engineering, with a research focus on modern control theory for robotics, spanning both optimal control (Model Predictive Control, and — going forward — reinforcement learning and deep reinforcement learning) and intelligent control (direct adaptive fuzzy control and deep neural-network-based control). My long-term interest is applying optimization-based and learning-based control methods to autonomous robotic systems.

Most of my work lives at the intersection of theory and simulation: deriving nonlinear system dynamics, designing controllers around them (from decoupled MIMO MPC formulations to state-augmented adaptive laws), and validating performance in MATLAB/Simulink — with Python for supporting analysis. I'm looking to grow this further by bringing RL/DRL into my optimal control work and deep neural networks into my intelligent control work.

I'm currently a Research Assistant at the Multi-Agent System Control (MASC) Laboratory, SEEE, HUST, supervised by Assoc. Prof. Hoai Nam Nguyen, where I work on control algorithms for robotic systems within a small team, currently centered on UAV flight control. My role centers on the modeling and controller-design side of the pipeline — building plant models, configuring and debugging MPC blocks, and implementing adaptive/learning-based control laws — working closely with teammates on validation and testing.

Outside of research, I enjoy exploring controller design more broadly across robotic platforms — from classical PID to self-tuning fuzzy and Lyapunov-based adaptive control — as a way to deepen my understanding of nonlinear control theory in practice.