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About

Dazhi Wang is a robotics and control systems researcher whose work spans autonomous robotic vehicles, industrial robot fault diagnosis, and advanced manipulator control. His most influential contribution, a two-layer trajectory tracking control scheme combining Extreme Learning Machines with Sliding Mode Control (ELM-SMC) for autonomous robotic vehicles, has garnered 37 citations since 2023, reflecting the growing relevance of intelligent control in autonomous systems. Wang has also made significant strides in industrial robot health monitoring, developing a multi-sensor fusion approach integrated with 1D Convolutional Neural Networks for fault diagnosis of servo systems—work that has accumulated 21 citations and addresses critical challenges in condition-based maintenance. Beyond diagnostics, his research explores sophisticated control algorithms, including repetitive control schemes using improved B-spline functions and fuzzy adaptive super-twisting sliding mode control for human-robot cooperative assembly lines. Earlier work on RBFNN-based servo system modeling using improved gravitational search algorithms demonstrates his long-standing interest in neural-network-driven robot performance optimization. Collectively, Wang's research bridges intelligent control theory and practical industrial robotics, making meaningful contributions to safer, more precise, and more autonomous robotic systems.