Abstract
In this paper, we proposed an adaptive-backstepping position control system for mobile manipulator robot (MMR). By applying recurrent fuzzy wavelet neural networks (RFWNNs) in the position-backstepping controller, the unknown-dynamics problems of the MMR control system are relaxed. In addition, an adaptive-robust compensator is proposed to eliminate uncertainties that consist of approximation errors and uncertain disturbances. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. The effectiveness of the proposed method is verified by comparative simulation results.
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