Neural networks control structure for manipulators with flexible last link

A novel neural networks control structure for manipulators with flexible last link was proposed The manipulator with flexible last link was regarded as two parts: the flexible latter part (FLP) that includes the last joint and the last link, and the rigid former part (RFP), i.e. the rest. The kinematical and dynamic equations of the two parts were derived apart. The proposed control approach combines organically a conventional model-based controller with a neural networks controller. The neural networks approximate the dynamic anti-model of FLP and decompose the desired trajectory of end point into two parts, desired trajectory of axis of the last joint and desired outer corner of the last joint. Those would be realized by conventional controllers. By combining, the approach has not only solved the problem of nonlinearity and calculation inefficiency of the dynamic model of a flexible manipulator but also lessen greatly the size of the neural network and thus improve the convergence speed The proposed approach achieves fine control effect on the emulation with a three links planar manipulator with a flexible last link