Tiannan Li

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About

Tiannan Li is a leading researcher in advanced control systems, with a primary focus on data-driven iterative learning control and precision motion systems. Their most significant contribution lies in developing a novel sliding mode iterative learning control method with an iteration-dependent parameter learning mechanism, specifically designed for nonlinear systems. This work, published in 2023 and already garnering 18 citations, addresses critical challenges in the tracking control of piezoelectric-actuated micro-positioning (PAMP) stages—a key technology in high-precision manufacturing and nanotechnology. By introducing a data-driven sliding mode iterative learning control (DDSILC) approach, Li has substantially improved convergence performance over traditional methods, enabling more accurate and reliable micro-positioning. This innovation not only advances theoretical understanding of iterative learning control but also offers practical solutions for real-world applications requiring extreme precision. Li’s research bridges the gap between control theory and industrial implementation, making their work highly influential in the fields of mechatronics and automation. With growing recognition in the control systems community, Tiannan Li continues to push the boundaries of nonlinear system control and precision engineering.