Papers
2
Total Citations
60
H-Index
2
About
Ziyu Lin is a rising researcher in robotics and autonomous systems, with a focus on trajectory planning and nonlinear optimal control. His work bridges the gap between traditional model-based methods and modern learning-based approaches, addressing fundamental challenges in constrained motion planning and real-time control. Lin’s most cited paper, "Local Learning Enabled Iterative Linear Quadratic Regulator for Constrained Trajectory Planning" (2022, 46 citations), introduces an efficient indirect method for handling nonlinear system dynamics in trajectory planning, enabling robust performance under constraints. His second highly cited work, "Recurrent Model Predictive Control: Learning an Explicit Recurrent Controller for Nonlinear Systems" (2022, 14 citations), proposes an offline control algorithm that serves as an explicit solver for traditional model predictive control, adaptively selecting optimal actions for large-scale nonlinear finite-horizon problems. These contributions demonstrate Lin’s ability to integrate learning with control theory, offering scalable solutions for complex autonomous systems. His research has significant implications for robotics, autonomous driving, and industrial automation, where real-time, constraint-aware decision-making is critical. With a growing citation record and innovative methodologies, Ziyu Lin is establishing himself as a key contributor to the next generation of intelligent control systems.
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