Papers
4
Total Citations
33
H-Index
4
About
Qingwen Ma is a rising researcher in the field of intelligent control and multi-agent systems, with a focus on the intersection of reinforcement learning, adaptive control, and robotics. Their work addresses fundamental challenges in coordinating autonomous systems, particularly in achieving robust and efficient formation control for mobile robots and multi-agent networks. Ma’s major contributions include pioneering the use of adaptive dynamic programming for self-learning sliding mode control of nonholonomic mobile robots, and developing a novel robust consensus control scheme that accelerates convergence rates in nonlinear multi-agent systems—a long-standing performance bottleneck. Their research also introduces kernel-based multiagent reinforcement learning for near-optimal formation control, and a distributed actor-critic learning approach for affine formation control of multi-robots with unknown dynamics, enabling maneuverability in complex environments. With key papers accumulating citations in the single digits to low teens, Ma’s work is gaining traction as foundational for next-generation autonomous coordination. Their 2023 paper on self-learning sliding mode control and 2022 work on convergence rate estimation represent notable achievements, demonstrating a clear trajectory toward practical, learning-based solutions for real-world robotic swarms.
Research Focus
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