Journal
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 46, Issue 6, Pages 759-770Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2015.2466194
Keywords
Adaptive control; barrier Lyapunov function; constraints; deadzone; neural networks; robotic manipulator
Funding
- National Natural Science Foundation of China [61522302, 61125306, 61520106009]
- National Basic Research Program of China (973 Program) [2014CB744206]
- National High Technology Research and Development Program of China (863 Program) [2015AA042304]
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In this paper, we present adaptive neural network tracking control of a robotic manipulator with input deadzone and output constraint. A barrier Lyapunov function is employed to deal with the output constraints. Adaptive neural networks are used to approximate the deadzone function and the unknown model of the robotic manipulator. Both full state feedback control and output feedback control are considered in this paper. For the output feedback control, the high gain observer is used to estimate unmeasurable states. With the proposed control, the output constraints are not violated, and all the signals of the closed loop system are semi-globally uniformly bounded. The performance of the proposed control is illustrated through simulations.
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