Abstract
In this paper, an adaptive neural network control with optimal number of hidden nodes and less computation is proposed for approximating the system uncertainty and tracking the trajectory of robot manipulators. Unlike the existing researches on adaptive neural network for robot manipulators, whose number of hidden nodes is fixed and determined through the trial and error, a new approach is proposed to obtain the optimal number of hidden nodes, in which the number of hidden nodes adapts to the trajectory variations and is capable of catching up with the optimal value and minimizing the tracking error. The proposed control scheme can avoid overfitting and underfitting problems and guarantee a better trajectory tracking. Mathematical proof for stability and convergence of the system is presented using Lyapunov method. In the end, simulations are performed to illustrate the effectiveness of the proposed approach.
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