Outputs (23)

A modified LSTM model for Chinese sign language recognition using leap motion (2022)
Conference Proceeding
Wu, B., Lu, Z., & Yang, C. (2022). A modified LSTM model for Chinese sign language recognition using leap motion. In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (1612-1617). https://doi.org/10.1109/SMC53654.2022.9945287

At present, there are about 70 million deaf people using sign language in the world, but for most normal people, it is difficult to understand the meaning of the sign language expression. Therefore, it is of great importance to explore the ways of re... Read More about A modified LSTM model for Chinese sign language recognition using leap motion.

Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance (2022)
Conference Proceeding
Huang, H., Lu, Z., Wang, N., & Yang, C. (2022). Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance. In 2022 27th International Conference on Automation and Computing (ICAC). https://doi.org/10.1109/ICAC55051.2022.9911082

A fixed-time adaptive neural network control scheme is designed for an unknown model manipulator system with input saturation and external environment disturbance, so that the system convergence time can be parameterized and not affected by the initi... Read More about Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance.

Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration (2022)
Conference Proceeding
Dai, H., Lu, Z., He, M., & Yang, C. (2022). Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration. In 2022 27th International Conference on Automation and Computing (ICAC). https://doi.org/10.1109/ICAC55051.2022.9911096

Learning from demonstration (LfD) has been developed and proved to be a promising method for transferring skill knowledge from human to robot. It is desired to have a demonstration device that can effectively map demonstrations to the robot's motion... Read More about Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration.

Biomimetic force and impedance adaptation based on broad learning system in stable and unstable tasks: Creating an incremental and explainable neural network with functional linkage (2022)
Journal Article
Lu, Z., & Wang, N. (in press). Biomimetic force and impedance adaptation based on broad learning system in stable and unstable tasks: Creating an incremental and explainable neural network with functional linkage. IEEE Robotics and Automation Magazine, 2-13. https://doi.org/10.1109/MRA.2022.3188218

This article presents a novel biomimetic force and impedance adaption framework based on the broad learning system (BLS) for robot control in stable and unstable environments. Different from iterative learning control, the adaptation process is real... Read More about Biomimetic force and impedance adaptation based on broad learning system in stable and unstable tasks: Creating an incremental and explainable neural network with functional linkage.

A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use (2022)
Conference Proceeding
Lu, Z., Wang, N., Li, M., & Yang, C. (2022). A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use. . https://doi.org/10.1109/ICCA54724.2022.9831826

Dynamic Movement Primitives (DMPs) is a general method for learning skills from demonstrations. Most previous research on DMP has focused on point to point skill learning and training, and the skills learned are usually generalized based on the same... Read More about A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use.

Robust output-feedback predictive control for proximity eddy current de-tumbling with constraints and uncertainty (2022)
Journal Article
Liu, X., Chang, H., Huang, P., & Lu, Z. (2023). Robust output-feedback predictive control for proximity eddy current de-tumbling with constraints and uncertainty. IEEE Transactions on Aerospace and Electronic Systems, 59(2), 858-870. https://doi.org/10.1109/TAES.2022.3191293

Proximity operation can significantly improve the efficiency of eddy current de-tumbling. However, the tumbling motion and non-cooperation of space debris make the chaser execute collision avoidance maneuvers and be influenced by model uncertainty. I... Read More about Robust output-feedback predictive control for proximity eddy current de-tumbling with constraints and uncertainty.

An adaptive fuzzy control for human-in-the-loop operations with varying communication time delays (2022)
Journal Article
Lu, Z., Guan, Y., & Wang, N. (2022). An adaptive fuzzy control for human-in-the-loop operations with varying communication time delays. IEEE Robotics and Automation Letters, 7(2), 5599-5606. https://doi.org/10.1109/LRA.2022.3158540

Time delay, especially varying time delay, is always an important factor affecting the stability to the human-in-the-loop system. Previous research usually focuses on the performance of the internal signal transmission part, but rarely considers the... Read More about An adaptive fuzzy control for human-in-the-loop operations with varying communication time delays.

Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system (2021)
Journal Article
Lu, Z., Wang, N., Li, M., & Yang, C. (2022). Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system. IEEE Transactions on Fuzzy Systems, 30(6), 1506-1515. https://doi.org/10.1109/tfuzz.2021.3136933

Different from previous work on single skill learning from human demonstrations, an incremental motor skill learning, generalization and control method based on dynamic movement primitives (DMP) and broad learning system (BLS) is proposed for extract... Read More about Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system.

DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation (2021)
Journal Article
Lu, Z., Wang, N., & Shi, D. (2022). DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation. Complex and Intelligent Systems, 8, 2873–2882. https://doi.org/10.1007/s40747-021-00429-3

Dual-arm robot manipulation is applicable to many domains, such as industrial, medical, and home service scenes. Learning from demonstrations (LfD) is a highly effective paradigm for robotic learning, where a robot learns from human actions directly... Read More about DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation.

A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks (2021)
Journal Article
Lu, Z., Wang, N., & Yang, C. (2022). A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks. International Journal of Systems Science, 53(1), 25-39. https://doi.org/10.1080/00207721.2021.1936275

Power consumption and data redundancy of wireless sensor networks (WSN) are widely considered for a distributed state monitoring network. For reducing the energy consumption and data amount, we propose a topology optimisation and an iterative paramet... Read More about A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks.