Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint
提出一种自适应神经网络跟踪控制方法，解决机器人操作臂在输入死区和输出约束下的控制问题，通过障碍李雅普诺夫函数处理输出约束，仿真验证了控制效果。
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.