Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints
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- 2 April 2015
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Cybernetics
- Vol. 46 (3), 620-629
- https://doi.org/10.1109/tcyb.2015.2411285
Abstract
This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore-Penrose inverse term is employed in order to prevent the violation of the full-state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.
Keywords
Funding Information
- National Natural Science Foundation of China (61203057, 61403063)
- National Basic Research Program of China (2014CB744206)
- National High Technology Research and Development Program of China (2015AA042304)
- Fundamental Research Funds for the China Central Universities of University of Electronic Science and Technology of China (ZYGX2013Z003)
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