Abstract
In this paper, an adaptive radial basis function neural network(RBFNN) backstepping controller is presented for a two-link robot manipulator in the presence of uncertainty and external interferences. RBFNNs are applied to approximate uncertain nonlinear functions, and considering the backstepping technique, an adaptive RBFNN backstepping control strategy is proposed. It is proved by the Lyapunov function that tracking errors converge to a small neighborhood of the equilibrium point and the closed-loop system variables are bounded. The simulation results demonstrate the effectiveness of the presented design method.
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