From a mechatronic point of view, the performance of electromechanical motion systems can be improved by changing both the mechanical design and the controller. The design of a controller is generally based on a model of the plant. Thus, to improve the controller, a more accurate model of the plant is required. When the structure is not known or when many parameters cannot be determined, learning control may be considered. A simple yet powerful learning control scheme that is suitable for electro-mechanical motion systems is Learning Feed-Forward Control. In this paper an overview is given of applications that have been reported concerning this scheme. Also, relations are listed with alternative learning control schemes that are in some sense alike.

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For motion control, learning feedforward controllers (LFFCs) should be applied when accurate process modelling is difficult. When controlling such processes with LFFCs in the form of multidimensional B-spline networks, large network sizes and a poor generalising ability may result, known as the curse of dimensionality. Therefore, a parsimonious (reduced dimensionality) LFFC is required. Empirical modelling methods are not suited to obtain parsimonious networks for highly nonlinear processes because large data sets are needed. Alternatively, (qualitative) process knowledge can be used to construct parsimonious LFF controllers. In the research reported, a parsimonious LFFC has been applied to a linear motor motion system. Experiments showed fast learning, good network parsimony, and small tracking errors for a range of motions.

Servo control is usually done by means of model-based feedback controllers, which has two difficulties. Firstly, the design of a well performing feedback controller requires extensive and time consuming modelling of the process. Secondly, by applying feedback control a compromise has to be made between performance and robust stability. The learning feed forward controller (LFFC) may help to overcome these difficulties. The LFFC consists of a feedback and a feed forward controller. The feedback controller is designed such that robust stability is guaranteed, while the performance is obtained by the feed forward controller. The feed forward 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 i s 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 obtained.

In this paper, a learning control system is considered for motion systems that are subject to two types of disturbances; reproducible disturbances, that re-occur each run in the same way, and random disturbances. In motion systems, a large part of the disturbances appear to be reproducible. In the control system considered, the reproducible disturbances are compensated by a learning component consisting of a B-spline neural network that is operated in feed-forward. The paper presents an analysis of stability properties of the con"guration in case of a linear process and second-order B-splines. The outcomes of the analysis are quantitative criteria for selection of the width of the B-splines, and of the learning rate, for which the system is guaranteed to be stable. These criteria facilitate the design of a learning feed-forward controller.