Yinling Jiang

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About

Dr. Yinling Jiang is a leading researcher in advanced control systems for robotics, with a primary focus on the robust and adaptive control of robotic manipulators. Her work masterfully integrates sliding mode control (SMC) with neural networks and adaptive backstepping techniques to address the critical challenge of unpredictable environmental changes. Dr. Jiang’s most influential contribution, the "Multi-model neural network sliding mode control for robotic manipulators" (2014, 6 citations), proposes a novel MNNSMC scheme that combines SMC’s robustness with neural network adaptability, ensuring stable performance even when a robot’s working environment shifts abruptly. Building on this, her "Multi-model back-stepping sliding mode control of Robotic Manipulators" (2014, 3 citations) further enhances system resilience by fusing backstepping with SMC. These multi-model frameworks are pivotal for real-world applications where robots must transition seamlessly between different operational conditions, such as in manufacturing or hazardous environments. Dr. Jiang’s work stands out for its practical, solution-oriented approach, offering engineers a powerful toolkit to design manipulators that are both precise and robust. Her research continues to inspire new generations of control systems, making her a notable figure in the field of robotic dynamics and automation.