Adaptive Neural Control for Robotic Manipulators With Output Constraints and Uncertainties
This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse t...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 29; no. 11; pp. 5554 - 5564 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2162-237X 2162-2388 2162-2388 |
| DOI | 10.1109/TNNLS.2018.2803827 |
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| Abstract | This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control. |
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| AbstractList | This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control. This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control.This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control. |
| Author | Yin, Zhao Zhang, Shuang Peng, Kaixiang Ouyang, Yuncheng Dong, Yiting |
| Author_xml | – sequence: 1 givenname: Shuang orcidid: 0000-0002-8314-9286 surname: Zhang fullname: Zhang, Shuang email: zhangshuang.ac@gmail.com organization: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China – sequence: 2 givenname: Yiting orcidid: 0000-0003-4018-4177 surname: Dong fullname: Dong, Yiting organization: Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA – sequence: 3 givenname: Yuncheng surname: Ouyang fullname: Ouyang, Yuncheng organization: School of Automation Engineering and Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China – sequence: 4 givenname: Zhao surname: Yin fullname: Yin, Zhao organization: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China – sequence: 5 givenname: Kaixiang orcidid: 0000-0001-8314-3047 surname: Peng fullname: Peng, Kaixiang organization: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29994076$$D View this record in MEDLINE/PubMed |
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| Snippet | This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position.... |
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| SubjectTerms | Adaptive control Artificial neural networks barrier Lyapunov function (BLF) Computer simulation Constraint modelling constraints Control design Control methods Control stability Feasibility studies Feedback Feedback control Liapunov functions Manipulator dynamics Manipulators Neural networks neural networks (NNs) Output feedback robot Robot arms Robots Stability analysis State feedback Systems stability Uncertainty |
| Title | Adaptive Neural Control for Robotic Manipulators With Output Constraints and Uncertainties |
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