Adaptive motion/force control strategy for non-holonomic mobile manipulator robot using recurrent fuzzy wavelet neural networks

In our study, we develop an adaptive position tracking system and a force control strategy for non-holonomic mobile manipulator robot, which combine the merits of Recurrent Fuzzy Wavelet Neural Networks (RFWNNs). In order to deal with the unknown knowledge problems of the robotic system, an adaptive RFWNNs control scheme with the dynamic structure and online learning ability is utilized to approximate unknown dynamics without the requirement of prior controlled system information. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, disturbances. According to the adaptive position tracking control design, an adaptive robust controller is also considered for the non-holonomic constraint force. The design of the adaptive online learning algorithms is derived by using the Lyapunov stability theorem. Therefore, the proposed controllers prove that they not only can guarantee the stability but also the tracking performance of the mobile manipulator robot control system. The effectiveness and robustness of the proposed method are demonstrated by comparative simulation and experimental results that are implemented in an indoor cleaning crawler-type mobile manipulator robot system.