Juchen Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Juchen Li has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Dongsheng Ma
School of Intelligent Manufacturing, Anhui Wenda University of Information Engineering, Hefei 231201, China
1 shared publication - Li Ren
School of Intelligent Manufacturing, Anhui Wenda University of Information Engineering, Hefei 231201, China
1 shared publication - Mahmud Iwan Solihin
Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia
1 shared publication - Tianli Li
School of Intelligent Manufacturing, Anhui Wenda University of Information Engineering, Hefei 231201, China
1 shared publication
Latest Publications
- Open Access
Fixed-Time Path Tracking Control of Uncertain Robotic Manipulator Based on Adaptive Deviation Correction and Compensation Mechanism Neural Network
A fixed-time sliding mode controller based on an adaptive neural network is developed for the path tracking problem of robotic manipulators with model uncertainty and external nonlinear interference. Firstly, a fixed-time sliding surface and sliding mode reaching law are designed based on the dynamic model of the robotic manipulator, which ensures that the error signal converges along the sliding surface within a fixed time. The speed of the state approaching the sliding surface can be flexibly adjusted through the reaching law, and it has strong robustness to parameter perturbations and external disturbances. Then, the uncertainty of model parameters and external disturbances is regarded as composite interference, and an adaptive neural network is utilized to approximate the disturbance online for adaptive fitting. This does not require precise modelling, the control input jitter is reduced, the composite disturbance is compensated in real time, and the system tracking accuracy is improved. Subsequently, the fixed-time stability characteristics of the closed-loop system are demonstrated through Lyapunov stability theory. Finally, the effectiveness and robustness of the proposed control strategy are verified through simulation.
Open AccessFixed-Time Path Tracking Control of Uncertain Robotic Manipulator Based on Adaptive Deviation Correction and Compensation Mechanism Neural Network
A fixed-time sliding mode controller based on an adaptive neural network is developed for the path tracking problem of robotic manipulators with model uncertainty and external nonlinear interference. Firstly, a fixed-time sliding surface and sliding mode reaching law are designed based on the dynamic model of the robotic manipulator, which ensures that the error signal converges along the sliding surface within a fixed time. The speed of the state approaching the sliding surface can be flexibly adjusted through the reaching law, and it has strong robustness to parameter perturbations and external disturbances. Then, the uncertainty of model parameters and external disturbances is regarded as composite interference, and an adaptive neural network is utilized to approximate the disturbance online for adaptive fitting. This does not require precise modelling, the control input jitter is reduced, the composite disturbance is compensated in real time, and the system tracking accuracy is improved. Subsequently, the fixed-time stability characteristics of the closed-loop system are demonstrated through Lyapunov stability theory. Finally, the effectiveness and robustness of the proposed control strategy are verified through simulation.