  Increasing demands in performance and quality make drive systems fundamental
parts in the progressive automation of industrial processes. Their conventional
models become inappropriate and have limited scope if one requires a precise
and fast performance. So, it is important to incorporate learning capabilities
into drive systems in such a way that they improve their accuracy in realtime,
becoming more autonomous agents with some degree of intelligence. To
investigate this challenge, this chapter presents the development of a learning
control system that uses neuro-fuzzy techniques in the design of a tracking
controller to an experimental electro-hydraulic actuator. We begin the chapter
by presenting the neuro-fuzzy modeling process of the actuator. This part
surveys the learning algorithm, describes the laboratorial system, and presents
the modeling steps as the choice of actuator representative variables, the
acquisition of training and testing data sets, and the acquisition of the
neuro-fuzzy inverse-model of the actuator. In the second part of the chapter,
we use the extracted neuro-fuzzy model and its learning capabilities to design
the actuator position controller based on the feedback-error-learning
technique. Through a set of experimental results, we show the generalization
properties of the controller, its learning capability in actualizing in
realtime the initial neuro-fuzzy inverse-model, and its compensation action
improving the electro-hydraulics tracking performance.
