An efficient neural network control for manipulator trajectory tracking with output constraints

This paper proposes a trajectory tracking scheme for a constrained manipulator with unknown dynamics is investigated, aiming to track the reference trajectory considering the output state constraints as well as unknown external disturbances. First, a modified tan-type barrier lyapunov function(BLF) is utilized to tackle the effect of constraint. Then, uncertainties are compensated with a radical basis function neural network(RBF-NN), the input number of which is reduce so as to construct a simplified neural network. Besides, boundary theory is also adopted in this paper to eliminate the chattering problem. Finally, the simulation results verify the following three aspects: 1) the constrained controller is able to guarantee the output states subject to the output state constraints; 2) the control scheme with the simplified RBF-NN performs almost the same as the one exactly knows the manipulators dynamics during free space motion; 3) the proposed controller shows robustness in the presence of unknown disturbances.