Decoupling of macro-mini manipulator using adaptive neural networks

Attaching a small manipulator (mini) with fast dynamic response at the end of a bigger manipulator (macro) with larger workspace leads to the concept of macro-mini manipulator, which is seen as a way to improve the system performance as compared to the macro manipulator acting alone, for example in terms of positioning accuracy. However, cross coupling between the two counterparts could undermine the practicality of the concept. In this paper, an adaptive neural network decoupler is presented to reduce the coupling effect of the macro-mini manipulators, without the need to have a proper dynamic model of the macro, and without alteration to the macro's controller. The stability of the proposed scheme is analyzed through the use of Lyapunov criterion. Simulation results show that by using the proposed neural network decoupler, the positioning accuracy of the macro-mini system can be improved significantly even when the macro manipulator is perturbed by external disturbances.