Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 46; no. 6; pp. 759 - 770
• Main Authors: Wei He, David, Amoateng Ofosu, Zhao Yin, Changyin Sun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive systems; barrier Lyapunov function; Computer simulation; constraints; deadzone; Estimates; Feedback control; Lyapunov methods; Manipulator dynamics; Manipulators; Neural networks; Output feedback; Robot arms; robotic manipulator; Robotics; robotic manipulator; deadzone; Adaptive control; constraints; barrier Lyapunov function; neural networks
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2015.2466194

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.