All Outputs (97)

Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators (2019)
Journal Article
Su, H., Qi, W., Yang, C., Aliverti, A., Ferrigno, G., & De Momi, E. (2019). Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators. IEEE Access, 7, 124207-124216. https://doi.org/10.1109/ACCESS.2019.2937380

© 2013 IEEE. Human-like behavior has emerged in the robotics area for improving the quality of Human-Robot Interaction (HRI). For the human-like behavior imitation, the kinematic mapping between a human arm and robot manipulator is one of the popular... Read More about Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators.

MPC-based 3-D trajectory tracking for an autonomous underwater vehicle with constraints in complex ocean environments (2019)
Journal Article
Zhang, Y., Liu, X., Luo, M., & Yang, C. (2019). MPC-based 3-D trajectory tracking for an autonomous underwater vehicle with constraints in complex ocean environments. Ocean Engineering, 189, Article 106309. https://doi.org/10.1016/j.oceaneng.2019.106309

© 2019 Elsevier Ltd This paper presents a novel three-dimension (3-D) underwater trajectory tracking method for an autonomous underwater vehicle (AUV) using model predictive control (MPC). First, the 6-degrees of freedom (DoF) model of a fully-actuat... Read More about MPC-based 3-D trajectory tracking for an autonomous underwater vehicle with constraints in complex ocean environments.

Bio-inspired approach for long-range underwater navigation using model predictive control (2019)
Journal Article
Zhang, Y., Liu, X., Luo, M., & Yang, C. (2021). Bio-inspired approach for long-range underwater navigation using model predictive control. IEEE Transactions on Cybernetics, 51(8), 4286-4297. https://doi.org/10.1109/TCYB.2019.2933397

Lots of evidence has indicated that many kinds of animals can achieve goal-oriented navigation by spatial cognition and dead reckoning. The geomagnetic field (GF) is a ubiquitous cue for navigation by these animals. Inspired by the goal-oriented navi... Read More about Bio-inspired approach for long-range underwater navigation using model predictive control.

Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems (2019)
Journal Article
Na, J., Yang, J., Wang, S., Gao, G., & Yang, C. (2021). Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems. IEEE Transactions on Systems Man and Cybernetics: Systems, 51(6), 3832-3843. https://doi.org/10.1109/tsmc.2019.2931627

Most existing adaptive control designs for nonlinear pure-feedback systems have been derived based on backstepping or dynamic surface control (DSC) methods, requiring full system states to be measurable. The neural networks (NNs) or fuzzy logic syste... Read More about Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems.

Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation (2019)
Journal Article
He, W., Sun, Y., Yan, Z., Yang, C., Li, Z., & Kaynak, O. (2020). Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation. IEEE Transactions on Neural Networks and Learning Systems, 31(5), 1735-1746. https://doi.org/10.1109/tnnls.2019.2923241

In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters acc... Read More about Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation.

Neural adaptive global stability control for robot manipulators with time-varying output constraints (2019)
Journal Article
Fan, Y., Kang, T., Wang, W., & Yang, C. (2019). Neural adaptive global stability control for robot manipulators with time-varying output constraints. International Journal of Robust and Nonlinear Control, 29(16), 5765-5780. https://doi.org/10.1002/rnc.4690

© 2019 John Wiley & Sons, Ltd. In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time-varying parameter that ins... Read More about Neural adaptive global stability control for robot manipulators with time-varying output constraints.

Uncertainty and disturbance estimator-based control of a flapping-wing aerial vehicle withwith unknown backlash-like hysteresis (2019)
Journal Article
Yin, Z., He, W., Kaynak, O., Yang, C., Cheng, L., & Wang, Y. (2020). Uncertainty and disturbance estimator-based control of a flapping-wing aerial vehicle withwith unknown backlash-like hysteresis. IEEE Transactions on Industrial Electronics, 67(6), 4826-4835. https://doi.org/10.1109/tie.2019.2926055

Robust and accurate control of a flapping-wing aerial vehicle (FWAV) system is a challenging problem due to the existence of backlash-like hysteresis nonlinearity. This paper proposes uncertainty and disturbance estimator (UDE)-based control with out... Read More about Uncertainty and disturbance estimator-based control of a flapping-wing aerial vehicle withwith unknown backlash-like hysteresis.

Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks (2019)
Journal Article
Yang, C., Peng, G., Cheng, L., Na, J., & Li, Z. (2019). Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks. IEEE Transactions on Systems Man and Cybernetics: Systems, 51(5), 3282-3292. https://doi.org/10.1109/tsmc.2019.2920870

In this paper, a force sensorless control scheme based on neural networks (NNs) is developed for interaction between robot manipulators and human arms in physical collision. In this scheme, the trajectory is generated by using geometry vector method... Read More about Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks.

A robot learning method with physiological interface for teleoperation systems (2019)
Journal Article
Luo, J., Yang, C., Su, H., & Liu, C. (2019). A robot learning method with physiological interface for teleoperation systems. Applied Sciences, 9(10), Article 2099. https://doi.org/10.3390/app9102099

The human operator largely relies on the perception of remote environmental conditions to make timely and correct decisions in a prescribed task when the robot is teleoperated in a remote place. However, due to the unknown and dynamic working environ... Read More about A robot learning method with physiological interface for teleoperation systems.

Estimation of EMG-Based force using a neural-network-based approach (2019)
Journal Article
Luo, J., Liu, C., & Yang, C. (2019). Estimation of EMG-Based force using a neural-network-based approach. IEEE Access, 7, 64856-64865. https://doi.org/10.1109/ACCESS.2019.2917300

© 2013 IEEE. The dynamics of human arms has a high impact on the humans' activities in daily life, especially when a human operates a tool such as interactions with a robot with the need for high dexterity. The dexterity of human arms depends largely... Read More about Estimation of EMG-Based force using a neural-network-based approach.

A Wave Variable Approach with Multiple Channel Architecture for Teleoperated System (2019)
Journal Article
Luo, J., Liu, C., Wang, N., & Yang, C. (2019). A Wave Variable Approach with Multiple Channel Architecture for Teleoperated System. IEEE Access, 7, 143912-143920. https://doi.org/10.1109/ACCESS.2019.2945674

© 2013 IEEE. Performance of teleoperation can be greatly influenced by time delay in the process of tele-manipulation with respect to accuracy and transparency. Wave variable is an effective algorithm to achieve a good stable capability. However, som... Read More about A Wave Variable Approach with Multiple Channel Architecture for Teleoperated System.

Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration (2019)
Journal Article
Zeng, C., Yang, C., Zhong, J., & Zhang, J. (2019). Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration. IEEE Access, 7, 145604-145613. https://doi.org/10.1109/ACCESS.2019.2945484

© 2013 IEEE. Learning a task such as pushing something, where the constraints of both position and force have to be satisfied, is usually difficult for a collaborative robot. In this work, we propose a multimodal teaching-by-demonstration system whic... Read More about Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration.

A neural-network-based controller for piezoelectric-actuated stick-slip devices (2017)
Journal Article
Cheng, L., Liu, W., Yang, C., Huang, T., Hou, Z. G., & Tan, M. (2018). A neural-network-based controller for piezoelectric-actuated stick-slip devices. IEEE Transactions on Industrial Electronics, 65(3), 2598-2607. https://doi.org/10.1109/TIE.2017.2740826

© 1982-2012 IEEE. Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that is composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the end-effector can slip on th... Read More about A neural-network-based controller for piezoelectric-actuated stick-slip devices.

Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities (2017)
Journal Article
Cui, R., Chen, L., Yang, C., & Chen, M. (2017). Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities. IEEE Transactions on Industrial Electronics, 64(8), 6785-6795. https://doi.org/10.1109/TIE.2017.2694410

© 2017 IEEE. This paper develops a novel integral sliding mode controller (ISMC) for a general type of underwater robots based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). The difficulties associated with the unmeasured v... Read More about Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities.

Development of a dynamics model for the Baxter robot (2016)
Presentation / Conference Contribution
Smith, A., Yang, C., Li, C., Ma, H., & Zhao, L. (2016). Development of a dynamics model for the Baxter robot. . https://doi.org/10.1109/ICMA.2016.7558740

© 2016 IEEE. The dynamics model of a robot is important to find the relation between the joint actuator torques and the resulting motion. There are two common methods to do this: The Lagrange formulation, which gives a closed form of the dynamics equ... Read More about Development of a dynamics model for the Baxter robot.

Neural-Learning-Based Telerobot Control with Guaranteed Performance (2016)
Journal Article
Yang, C., Wang, X., Cheng, L., & Ma, H. (2017). Neural-Learning-Based Telerobot Control with Guaranteed Performance. IEEE Transactions on Cybernetics, 47(10), 3148-3159. https://doi.org/10.1109/TCYB.2016.2573837

© 2013 IEEE. In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic leve... Read More about Neural-Learning-Based Telerobot Control with Guaranteed Performance.

Dual adaptive control of bimanual manipulation with online fuzzy parameter tuning (2014)
Presentation / Conference Contribution
Smith, A., Yang, C., Ma, H., Culverhouse, P., Cangelosi, A., & Burdet, E. (2014). Dual adaptive control of bimanual manipulation with online fuzzy parameter tuning. . https://doi.org/10.1109/ISIC.2014.6967605

© 2014 IEEE. A biomimetic controller with online adaptation of impedance and force is applied to a full kinematic and dynamic model of the Baxter bimanual robot. A set of fuzzy logic engines are proposed to infer the values of tuning gains which affe... Read More about Dual adaptive control of bimanual manipulation with online fuzzy parameter tuning.