Papers
15
Total Citations
161
H-Index
7
About
Dang Xuan Ba is a leading researcher in advanced control systems for robotics and electromechanical systems, with a primary focus on developing intelligent, adaptive controllers for robotic manipulators and motor systems. His major contributions lie in creating robust control frameworks that combine neural networks, sliding mode control, and adaptive gain-learning mechanisms to achieve high-precision position tracking under uncertain and constrained conditions. His most cited work, "Gain-Adaptive Robust Backstepping Position Control of a BLDC Motor System" (41 citations), introduces a dual-loop controller that enhances robustness and adaptability, while his direct robust nonsingular terminal sliding mode controller (30 citations) further advances servomotor rigid robot control. Ba has also explored gait optimization for quadruped robots using evolutionary computation (20 citations) and developed neural flexible PID controllers for task-space control (15 citations). His innovative approaches to learning-based control, including iterative second-order neural-network learning and intelligent sliding mode controllers with output constraints, have garnered over 140 total citations, establishing him as a key contributor to the next generation of adaptive, high-performance robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Gain-Adaptive Robust Backstepping Position Control of a BLDC Motor System41 citations · 2018
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- 3Gait Optimization of a Quadruped Robot Using Evolutionary Computation20 citations · 2021
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