Papers
5
Total Citations
74
H-Index
4
About
Qiuzhen Yan is a leading researcher in advanced control systems, with a primary focus on intelligent learning control for robotic and bio-inspired systems. Their work addresses fundamental challenges in trajectory tracking for robot manipulators, particularly overcoming the practical limitations of arbitrary and random initial errors—a critical barrier in real-world deployment. Yan’s major contributions include pioneering neural network-based adaptive iterative learning control schemes that relax the stringent zero-initial-error condition, introducing time-varying boundary layers and error-tracking strategies. This body of work has garnered significant attention, with their most-cited paper, "Neural Network-Based Adaptive Learning Control for Robot Manipulators With Arbitrary Initial Errors" (2019), accumulating 38 citations. Yan has further extended these robust learning control methods to complex systems such as pneumatic artificial muscle (PAM) systems, which are vital for biomimetic robots and medical assistive devices, and has tackled challenging nonlinearities including input deadzone. Their research consistently demonstrates a practical, Lyapunov-based approach to ensure stability and convergence, marking Yan as a key innovator in making adaptive iterative learning control viable for high-precision, real-world robotic applications.
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