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About

Shouxu Zhang is a leading researcher in bio-inspired robotics and intelligent control systems, with a focus on underwater locomotion and multi-agent coordination. His work bridges the gap between theoretical control algorithms and practical robotic applications, particularly in challenging aquatic environments. Zhang’s most impactful contribution is the development of terrain-adaptive locomotion control for underwater hexapod robots, where he pioneered the use of proprioceptive sensors to interpret leg–terrain interactions without relying on external cameras or sonars—a breakthrough for navigating unknown deformable terrains (9 citations). He also advanced formation control for groups of semi-biomimetic robotic fishes, designing a consensus-based leader-following algorithm that simplifies complex Euler–Lagrange dynamics for real-world deployment (6 citations). Additionally, Zhang has made notable strides in self-triggered adaptive neural network control for nonlinear systems with input constraints, reducing computational overhead while maintaining stability (5 citations). His work on modeling and dynamic control of semibiomimetic robotic fish further demonstrates his ability to simplify bio-inspired designs without sacrificing performance. With a growing citation record, Zhang’s research is shaping the future of autonomous underwater exploration and multi-robot systems.