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CONTROL OF AN ELECTRO-HYDRAULIC SYSTEM USING NEURO-FUZZY MODELLING AND REAL-TIME LEARNING APPROACHES

Drive systems are fundamental parts of industrial processes. Although, their conventional models and projected controllers based on those models are becoming not enough appropriate and having a limited scope. So, it is important incorporate learning capabilities into drive systems in such a way that they improve their accuracy in real-time, becoming more autonomous agents with some “intelligence degree”. To investigate this challenge, this paper shows the development of a position control system using neuro-fuzzy techniques to an experimental electro-hydraulic system. The paper describes the acquisition of the neuro-fuzzy inverse-model of the system with some practical questions like the acquisition of a “good” training data set, and the choice of actuator significant variables. The extracted inverse model and its learning capabilities are used to design the actuator position controller based on the feedback-error-learning technique. Through a set of experimental results, we show the generalisation capacity of the controller, its real-time learning capability in extract relations not incorporated into the initial inverse-model, and therefore improve its tracking performance. At last, it is our aim in present this paper not only to describe the good benefits of incorporate neurofuzzy methodologies into drive control systems, but also discuss and pointed out certain control situations where this methodology reveals some drawbacks.