Learning feedforward control of a flexible beam

Servo control is usually done by means of model-based feedback controllers, which has two difficulties: 1) the design of a well performing feedback controller requires extensive and time consuming modelling of the process; and 2) by applying feedback control a compromise has to be made between performance and robust stability. The learning feedforward controller (LFFC) may help to overcome these difficulties. The LFFC consists of a feedback and a feedforward controller. The feedback controller is designed such that robust stability is guaranteed, while the performance is obtained by the feedforward controller. The feedforward controller is a function approximator that is adapted on the basis of the feedback signal. The LFFC is applied to a flexible robot arm, which has complex dynamics and unknown properties, such as friction. A stability analysis of the (idealised) LFFC is presented. Simulation experiments (with a non-idealised LFFC) confirm the results of this analysis and show that without extensive modelling a good performance can be obtaine