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

2015; Institute of Electrical and Electronics Engineers; Volume: 46; Issue: 6 Linguagem: Inglês

10.1109/tsmc.2015.2466194

2168-2232

Wei He, David Ofosu Amoateng, Zhao Yin, Changyin Sun,

Advanced Control Systems Optimization

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.