In this work, we use a neural network as a substitute for the traditional analytic functions employed as an inversion set in feedback linearization control algorithms applied to hydraulic actuators. Although very efective and with strong stability guarantees, feedback linearization control depends on parameters that are difcult to determine, requiring large amounts of experimental efort to be identifed accurately. On the other hands, neural networks require little efort regarding parameter identifcation, but pose signifcant hindrances to the development of solid stability analyses and/or to the processing capabilities of the control hardware. Here, we combine these techniques to control the positioning of a hydraulic actuator, without requiring extensive identifcation procedures nor losing stability guarantees for the closed-loop system, at reasonable computing demands. The efectiveness of the proposed method is verifed both theoretically and by means of experimental results
