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About

Jielong Chen is a rising researcher in robotics and neural dynamics, whose work centers on advancing control algorithms for robotic manipulators. His primary research areas include time-variant nonlinear optimization, model-free robot control, and Zhang neurodynamics—a specialized branch of recurrent neural networks for solving time-varying problems. Chen’s major contribution lies in developing inverse-free and pseudoinverse-free neural network schemes that eliminate the computational burden and numerical instability of traditional Jacobian pseudoinverse methods. His most-cited paper, "Inverse-free zeroing neural network for time-variant nonlinear optimization with manipulator applications" (2024, 23 citations), introduces a groundbreaking approach that bypasses matrix inversion entirely, enabling faster and more robust real-time control. Building on this, his 2025 works—"Discrete Jacobian-Pseudoinverse-Free Zhang Neurodynamics Algorithm" (3 citations) and "Model-Free and Pseudoinverse-Free Zhang Neurodynamics Scheme" (2 citations)—tackle the critical challenge of path tracking for robotic arms with unknown or uncertain models, a fundamental problem in practical robotics. These achievements position Chen at the forefront of next-generation, computationally efficient robot control, offering elegant solutions that bridge theoretical neural dynamics with real-world manipulator applications.