Manh-Hung Ha

Papers

Total Citations

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About

Manh-Hung Ha is a leading researcher in nonlinear control systems and intelligent robotics, with a focus on adaptive and neural network-based control strategies for mobile platforms. His most-cited work, “Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks” (2023, 26 citations), introduces a novel ABHSMC controller that synergistically integrates backstepping control with a Radial Basis Function neural network and hierarchical sliding mode techniques. This pioneering approach significantly enhances trajectory tracking and disturbance rejection for three-wheeled mobile robots, addressing critical challenges in autonomous navigation under uncertain dynamics. Ha’s contributions lie in bridging classical control theory with modern machine learning, offering robust, real-time solutions for complex robotic systems. His work has been widely recognized for its practical applicability in autonomous vehicles and industrial automation, earning him a reputation as an innovator in adaptive control. With a growing citation impact, Ha continues to push the boundaries of intelligent control, making his research essential reading for students and engineers seeking to advance mobile robot autonomy.