This paper addresses the control of a manipulator with link flexibilities. The increased complexity in its dynamics presents challenges to controllers based on non-colocated sensing. In this paper a nonlinear predictive control approach is presented using a discrete time multilayer perceptron network model for the plant. The neural network model is trained to predict future outputs based on the available past measurements. At each sampling instant, the discrete time control input is calculated by minimizing a performance criterion. The method is compared to non-model based collocated PD control. Simulation results are presented.

An automatic control system for backhoe type excavators during free motion and digging operations is presented. Some of the uncertainties associated with the basically unstructured environment of soil digging tasks are dealt with by using an adaptive control system capable of on-line learning and control of the dynamic response over a wide range of parameter variations. The proposed control system comprises a primary and a secondary controller; the former is used to linearize the plant and the latter to compensate for modeling errors. The primary controller is implemented as a feedforward multilayer neural net trained to emulate the inverse dynamics of the plant. The secondary controller is a PID controller with the gains tuned so as to provide a satisfactory transient behavior. Simulation results are used to demonstrate the applicability of the proposed control scheme.