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About

Peng-Heng Yin is a researcher whose work bridges the frontiers of intelligent robotics and nonlinear control systems, with a particular focus on autonomous navigation and stability theory. His most influential contribution, the 2019 study on multi-layer feed-forward neural network deep learning control for mobile robot obstacle avoidance (37 citations), introduced a novel hybrid position and virtual-force algorithm that significantly enhances real-time navigation in dynamic environments. This work demonstrates how deep learning architectures can be effectively integrated with traditional control methods to improve robotic autonomy. Yin further advanced mobile robotics through his 2019 development of a combined A* and artificial potential field algorithm for path planning (16 citations), offering a practical solution to the longstanding challenge of balancing global optimization with local obstacle avoidance. In the domain of control theory, his 2018 stability analysis of nonlinear Takagi-Sugeno fuzzy systems (10 citations) provided rigorous frameworks for dynamic output feedback control with normalized membership functions, addressing critical stability issues in complex multi-subsystem configurations. These contributions collectively establish Yin as a versatile researcher whose work has practical implications for autonomous systems and theoretical significance for nonlinear control design.