Abstract: The robust control problem of mobile manipulators with unknown system dynamics is addressed. Firstly, the dynamics of mobile manipulator with external disturbances is constructed based on the primary system dynamics. In virtue of the infinite approximation ability of neural network, an estimator is designed to on-line identify the uncertainties of system structure. Then a neural network robust control scheme without apriori knowledge is proposed. The controller stability is proved in theory and also has the capability of disturbance-rejection in the presence of time varying bounded disturbances. The coordinated control of different dynamics parts: mobile platform and manipulator, is achieved. In addition, in order to reduce the computation burden of the neural network, a partitioned neural network structure is proposed to identify the two separate vectors of the structure uncertainties respectively. The training efficiency of the neural network is highly improved. Finally, computer simulation results validate the effect of the proposed controller, which can cooperatively track the system trajectory quickly and stably.

Key words: Neural network, Omnidirectional mobile manipulator, Robust control, Trajectory tracking