Papers
5
Total Citations
74
H-Index
3
About
Xianglong Liang is a leading researcher at the intersection of adaptive control, reinforcement learning, and nonlinear system dynamics, with a particular focus on advancing the autonomy and precision of hydraulic and robotic manipulators. His work is distinguished by pioneering the integration of learning-based methods with classical control theory to address the profound challenges posed by unmodeled dynamics and time-varying disturbances. Liang’s most impactful contributions include the development of a disturbance observer-based actor-critic learning control framework for uncertain nonlinear systems (28 citations) and an adaptive control strategy for n-link hydraulic manipulators that incorporates gravity and friction identification (30 citations). He has further advanced the field by proposing a reinforcement learning-based adaptive controller that guarantees asymptotic tracking for uncertain Euler-Lagrange systems (12 citations), and by exploring deep Lagrangian networks for robot dynamics modeling. His recent work on an inner-outer loop iterative learning control framework for hydraulic manipulators demonstrates his ongoing commitment to solving real-world engineering challenges. With a growing body of work that bridges theoretical rigor and practical application, Liang is establishing himself as a key figure in the next generation of intelligent control systems.
Research Focus
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