Welcome to the Model-free Autonomous Agent and Intelligent Control (MAgIC) Lab at ShanghaiTech University.
Our research spans the development of bio-inspired underwater and aerial robots—such as robotic fish and flapping-wing vehicles—with a focus on perception, planning, control, and multi-agent coordination.
Currently, the core of our work lies at advanced motion control for robotic systems operating in dynamic fluid environments.
Underlying these applications is our fundamental research into the modeling and control of complex, nonlinear, time-varying systems across multiple scales.
We develop adaptive, data-driven, and learning-based control theories with provable guarantees, tackling challenges such as uncertainty, disturbance, and system constraints.
Led by Prof. Yang Wang, our team consists of passionate PhD and master students dedicated to pushing the boundaries of fluidic-interactive robotic systems and intelligent control theory.
We welcome collaborations and discussions with researchers who share our interests.
News
06 June 2026
MAgIC Lab Student Team Wins Golden Award at the Shanghai “TiaoZhanBei” Competition
The team led by MAgIC Lab master's student Yuhang Zhao won the Golden Award at “TiaoZhanBei” Chinese College Students' Entrepreneurship Plan Competition.
This achievement marks a historic breakthrough for ShanghaiTech University, as Zhao's team became the first from the university to reach the Shanghai final of the highly competitive regular track and went on to claim the Golden Award.
03 June 2026
MAgIC Lab Students Present Latest Research at IEEE ICRA
MAgIC Lab Ph.D. student Xiaozhu Lin and graduate student Qinxiao Ma, both advised by Prof. Yang Wang, successfully attended IEEE ICRA and presented their latest research, receiving strong recognition from the robotics community.
Lin's work develops a bio-inspired reinforcement learning approach for agile and controllable fast-start maneuvers of robotic fish, while Ma's work introduces an ultra-fast deep photonic reservoir computing framework for feedforward dynamic compensation of UAVs in confined environments, together contributing to advances in bio-inspired robotics, aerial robotics, and intelligent robotic systems.
14 May 2026
A Historic Milestone for MAgIC Lab: Congratulations to Heng Zhang, Our First Ph.D. Graduate!
MAgIC Lab warmly congratulates Ph.D. student Heng Zhang on successfully completing the doctoral thesis defense and becoming the first Ph.D. graduate in the history of the lab. Heng Zhang conducted doctoral research under the supervision of Prof. Yang Wang and made significant contributions to the lab's research activities during the Ph.D. program.
As the first doctoral graduate of MAgIC Lab, Heng Zhang's achievement marks an important milestone in the development of the lab. We sincerely wish Heng Zhang continued success in future academic and professional endeavors.
14 Apr 2026
Congratulations to MAgIC Lab for two papers accepted by IFAC2026.
The papers entitled 'Switching-based Adaptive Feedforward Control for Uncertain Linear Multivariable Systems: Periodic Disturbance Cancellation' and 'Model Reference Adaptive Control without High-Frequency Gain Knowledge via Derivative Injection and Global HOSM Differentiators' have been accepted for presentation at the 23rd IFAC World Congress. The former proposes a switching-based adaptive feedforward approach to reject multi-sinusoidal disturbances in uncertain MIMO systems, while the latter presents a derivative-injection and HOSM differentiator enhanced MRAC scheme to achieve stable tracking for arbitrary relative-degree plants with unknown high-frequency gain.
