Tung Thanh Pham
Papers
1
Total Citations
5
H-Index
1
About
Tung Thanh Pham is a robotics and control systems researcher whose work centers on intelligent control algorithms for mobile robots, particularly omnidirectional platforms. His major contribution lies in optimizing neural network-based controllers through evolutionary computation. In his highly cited 2017 paper, Pham introduced a novel method that integrates Particle Swarm Optimization (PSO) with Radial Basis Function Neural Networks (RBFNN) for omnidirectional mobile robot control. This approach addresses a critical limitation of standard RBF networks—the difficulty in determining optimal network structure—by using PSO to automatically select the number of hidden neurons and tune network parameters. The resulting controller achieves superior tracking performance and adaptability compared to conventional designs. With over 5 citations, this work has influenced subsequent research in intelligent robotics control, demonstrating how bio-inspired optimization can enhance neural network performance in real-time applications. Pham’s research bridges the gap between theoretical optimization algorithms and practical robotic systems, offering a systematic methodology for designing efficient, self-tuning controllers. His contributions are particularly valuable for students and researchers exploring hybrid AI approaches in autonomous navigation and motion control.
Research Focus
Key Achievements
Top Papers
- 1