All Outputs (5)

Effect of velocity and acceleration in joint angle estimation for an EMG-Based upper-limb exoskeleton control (2021)
Journal Article
Tang, Z., Yu, H., Yang, H., Zhang, L., & Zhang, L. (2022). Effect of velocity and acceleration in joint angle estimation for an EMG-Based upper-limb exoskeleton control. Computers in Biology and Medicine, 141, Article 105156. https://doi.org/10.1016/j.compbiomed.2021.105156

Most studies on estimating user's joint angles to control upper-limb exoskeleton have focused on using surface electromyogram (sEMG) signals. However, the variations in limb velocity and acceleration can affect the sEMG data and decrease the angle es... Read More about Effect of velocity and acceleration in joint angle estimation for an EMG-Based upper-limb exoskeleton control.

Human Gait Data Augmentation and Trajectory Prediction for Lower-Limb Rehabilitation Robot Control Using GANs and Attention Mechanism (2021)
Journal Article
Wang, Y., Li, Z., Wang, X., Yu, H., Liao, W., & Arifoglu, D. (2021). Human Gait Data Augmentation and Trajectory Prediction for Lower-Limb Rehabilitation Robot Control Using GANs and Attention Mechanism. Machines, 9(12), Article 367. https://doi.org/10.3390/machines9120367

To date, several alterations in the gait pattern can be treated through rehabilitative approaches and robot assisted therapy (RAT). Gait data and gait trajectories are essential in specific exoskeleton control strategies. Nevertheless, the scarcity o... Read More about Human Gait Data Augmentation and Trajectory Prediction for Lower-Limb Rehabilitation Robot Control Using GANs and Attention Mechanism.

A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications (2021)
Journal Article
Zhao, H., Peng, L., Wu, P., & Yu, H. (2022). A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 236(5), 1038-1048. https://doi.org/10.1177/09596518211060976

This article proposes a novel distributed data-driven bipartite consensus tracking scheme for bipartite consensus tracking problems of multi-agent systems with bounded disturbances and coopetition networks. The proposed scheme only uses the input/out... Read More about A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications.

Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems (2021)
Journal Article
Zhao, H., Peng, L., & Yu, H. (2022). Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems. Applied Mathematics and Computation, 412, Article 126582. https://doi.org/10.1016/j.amc.2021.126582

This paper considers the data quantization problem for a class of unknown nonaffine nonlinear discrete-time multi-agent systems (MASs) under repetitive operations to achieve bipartite consensus tracking. Here, a quantized distributed model-free adapt... Read More about Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems.

Model-free adaptive consensus tracking control for unknown nonlinear multi-agent systems with sensor saturation (2021)
Journal Article
Zhao, H., Peng, L., & Yu, H. (2021). Model-free adaptive consensus tracking control for unknown nonlinear multi-agent systems with sensor saturation. International Journal of Robust and Nonlinear Control, 31(13), 6473-6491. https://doi.org/10.1002/rnc.5630

This article proposes a distributed model-free adaptive consensus tracking control (DMFACTC) approach for a class of unknown heterogeneous nonlinear discrete-time multi-agent systems (MASs) with sensor saturation and measurement disturbance to perfor... Read More about Model-free adaptive consensus tracking control for unknown nonlinear multi-agent systems with sensor saturation.