Neural-network-based variable structure control of electrohydraulic servosystems subject to huge uncertainties without persistent excitation

A novel scheme investigating a radial-basis-function neural network (RBFNN) with variable structure control (VSC) for electrohydraulic servosystems subject to huge uncertainties is presented. Although the VSC possesses some advantages (e.g., fast response, less sensitive to uncertainties, and easy implementation), the chattering control input often occurs. The reason for a chattering control input is that the switching control in the VSC is used to cope with the uncertainties. The larger the uncertainties which arise, the larger switching control occurs. In this paper, an RBFNN is employed to model the uncertainties caused by parameter variations, friction, external load, and controller. A new weight updating law using a revision of e-modification by a time varying dead zone can achieve an exponential stability without the assumption of persistent excitation for the uncertainties or radial basis function. Then, an RBFNN-based VSC is constructed such that some part of uncertainties are tackled, that the tracking performance is improved, and that the level of chattering control input is attenuated. Finally, the stability of the overall system is verified by the Lyapunov stability criterion