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In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control(ABFCNC) system for motion/force control of the mobile-manipulator robot(MMR) is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances.Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.
In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control (ABFCNC) system for motion / force control of the mobile-manipulator robot (MMR) is proposed. By applying the ABFCNC in the tracking-position controller , the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, unbalance disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Thus, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparatives imulation results.