A neural networks controller for a single-link flexible manipulator based on the inverse dynamics structure

Motivated by the well-known inverse dynamics control structure developed in the literature for flexible link manipulators, in this paper two multi-layer neural networks (NNs) are proposed to learn the nonlinearities of the system for achieving tip position trajectory tracking control for a single-link flexible manipulator. The re-defined output approach is used by feeding back this output to guarantee the minimum phase behavior of the resulting closed loop system. No a priori knowledge about the nonlinearities of the system is needed where the payload mass is also assumed to be unknown. The weights of the networks are adjusted using a modified online error backpropagation algorithm that is based on the propagation of output error, derivative of error and the tip deflection of the manipulator. The real-time controller is implemented on an experimental setup. The results achieved by the proposed neural network (NN) controller are compared experimentally with conventional PD and inverse dynamics controls to substantiate the advantages of our scheme and its promising potential