Neural Network Controller Design for an Uncertain Robot With Time-Varying Output Constraint
针对n连杆机器人位置受时变约束的问题，提出基于自适应控制和神经网络的控制器，利用时变障碍李雅普诺夫函数保证约束不违反，并通过仿真验证可行性。
An adaptive control-based neural network for a n-link robot is studied and the considered robot can be transformed as a class of multi-input-multioutput systems. The position of the robot or the output of the transformed systems is constrained in a time-varying compact set. It is commonly known that the constant constraint belongs to a special case of the time-varying constraint, and thus, it can be more general for handling practical problem as compared with the existing methods for robot. The neural approximation is used to estimate the unknown functions of systems and the time-varying barrier Lyapunov function is used to overcome the violation of constraints. It can prove the stability of the closed-loop systems by using Lyapunov analysis. The feasibility of the approach is demonstrated by performing a simulation example.