Yana Yang

Robust control barrier function-based shared control for cognitive-physical human-robot collaboration

Yana Yang, Zhijia Li, Huixin Jiang, Ying Zhang

Ensuring safe shared control in human - robot collaboration remains challenging due to uncertain human inputs and time-varying operator cognitive states. Existing methods primarily address either physical-interaction safety or authority allocation, but rarely provide a unified framework that simultaneously enables cognition-aware authority …

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Reinforcement learning-based fixed-time human-robot collaboration control without force sensors

Yana Yang, Qiang Zeng, Huixin Jiang, Junpeng Li

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Safe Reinforcement Learning Control for Unmanned Surface Vessel

Yana Yang, Chunyu Liu, Tengfei Zhang, Changchun Hua et autres

Unmanned surface vessel (USV) faces three core challenges in achieving safe, efficient, and adaptive autonomous navigation in complex dynamic waters. Reinforcement Learning (RL) policies are prone to violating physical constraints during exploration, leading to hazardous behaviors. Existing safety mechanisms struggle to ensure …

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Semi-Global Output-Feedback Control for Underactuated Nonlinear Systems Under Sensor Faults

Yana Yang, Wenqi Wang, Xiaoshuang Zhou, Changlong Liu et autres

This paper addresses the semi-global output feedback tracking control problem for a class of underactuated nonlinear systems with non-strict feedback structure and sensor faults. Firstly, an adaptive estimation mechanism is designed to reconstruct both the system states and the sensor fault parameters …

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Prescribed-Time Control for a Class of Underactuated Systems: A High-Order Fully Actuated System Approach

Xiaoshuang Zhou, Yana Yang, Changchun Hua, Tengfei Zhang et autres

For uncertain link-type underactuated mechanical systems (LUMSs), existing fully actuated system (FAS)-based approaches are often difficult to apply because the required structural conditions are not satisfied. This article develops an adaptive prescribed-time controller for a class of LUMSs by combining FAS theory …

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Enhanced safety control of underactuated mechanical systems: Application to cranes

Yana Yang, Changlong Liu, Xiaoshuang Zhou, Tengfei Zhang et autres

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Reinforcement Learning-Based Hybrid Control Approach for a Class of Underactuated Mechanical Systems

Tengfei Zhang, Yana Yang, Xiaoshuang Zhou, Ying Zhang et autres

Underactuated mechanical systems are pivotal in industrial applications due to their lightweight design and flexibility. Most control methods designed for such systems are either tailored to specific types or feature complex continuous controller structures, whose discretization in practical implementations may lead to …

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Adaptive Irregular Time-Varying Constraints Control for a Class of Underactuated Mechanical Systems

Changlong Liu, Yana Yang, Changchun Hua, Junpeng Li

State constraints are crucial for preventing system hazards by enforcing safety boundaries, yet ensuring state constraints for underactuated mechanical systems (UMSs) operating in complex environments remains unsolved due to their limited workspace and inherent underactuation. To address this critical safety issue involving …

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Adaptive Complexity Nonlinear Hybrid-Model Predictive Control for Underactuated USVs With Experimental Validation

Yana Yang, Long Chen, Junpeng Li

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Learning from N-Tuple Data with M Positive Instances: Unbiased Risk Estimation and Theoretical Guarantees

Miao Zhang, Junpeng Li, Changchun Hua, Yana Yang

Weakly supervised learning often operates with coarse aggregate signals rather than instance labels. We study a setting where each training example is an $n$-tuple containing exactly m positives, while only the count m per tuple is observed. This NTMP (N-tuple with M …

Predefined-time sliding mode trajectory tracking control for asymmetric underactuated surface vehicle with nonlinear disturbances

Chenliang Hao, Yana Yang, Jianliang Zhang, Guanglei Zhao

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Prescribed-Time Cooperative Control of Multilateral Teleoperation Systems: A Novel Composite Fuzzy Learning-Based Approach

Huixin Jiang, Yana Yang, Changchun Hua, Junpeng Li

In this paper, a novel prescribed-time composite learning-enhanced fuzzy (PrTCLF) cooperative control approach is proposed for the multiple-master/multiple-slave(MMMS) teleoperation systems in presence of system model uncertainties and external interferences. Firstly, compared with traditional MMMS systems, a unifying virtual master-slave teleoperation control framework, …

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