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About

Yuliang Cai is a leading researcher in intelligent control systems, with a primary focus on adaptive fault-tolerant control, multi-agent formation coordination, and reinforcement learning (RL)-based optimization. His most influential work, an adaptive fuzzy fault-tolerant tracking control method published in 2019 (128 citations), pioneered the integration of integral RL with Takagi-Sugeno fuzzy models to handle partially unknown systems suffering from actuator faults—a critical challenge in real-world autonomous systems. Building on this, Cai advanced the field of multi-agent systems by proposing a novel mixed self- and event-triggered control strategy (2021, 61 citations), enabling fully distributed formation control for general linear agents while dramatically reducing communication and computational overhead. His recent work on formation control with obstacle avoidance for multi-robot systems (2023) addresses practical deployment challenges, bridging theory and application in robotics. With over 190 total citations across his core publications, Cai’s contributions are shaping the next generation of resilient, autonomous, and cooperative control systems, making him a notable figure in the intersection of fuzzy logic, RL, and networked robotics.