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About

Jiesen Pan is a researcher focused on the intersection of learning-based control, autonomous systems, and safety-critical robotics. Their work addresses fundamental challenges in enabling robots—from quadrotors to self-driving cars—to operate reliably under uncertain, real-world conditions. Pan’s major contributions lie in developing adaptive, predictive control frameworks that integrate machine learning to handle model uncertainties and external disturbances. Notably, their 2021 paper on "Learning-Based Predictive Path Following Control for Nonlinear Systems" (35 citations) introduces a novel approach that combines learning with predictive control to achieve high-performance path following despite uncertain disturbances. Another key contribution is the "Learning-Based Safety-Stability-Driven Control" framework (16 citations), which simultaneously ensures safety and tracking stability for safety-critical systems—a crucial advance for autonomous mobile robots and industrial manipulators. Pan also demonstrated practical impact through their work on quadrotors under wind disturbances (10 citations), presenting a safety-preserving cascaded quadratic programming controller. With a growing citation record, Pan’s research is shaping how autonomous systems can learn to navigate unpredictable environments while maintaining rigorous safety guarantees—a vital step toward trustworthy deployment of robots in the real world.