Learning From Examples to Compensate Nonlinearities in Electromechanical Drives: a Fuzzy-Logic Approach

Incorporate learning mechanisms into electromechanical drives permits design systems with self-learning capabilities and produces more autonomous processes with some “intelligence” degree. This paper presents a control system that uses a fuzzy learning methodology to design an inverse-model compensation controller. The controller shows generalisation and learning capabilities to compensate non-linear terms that affect the system dynamics. To investigate the controller, an experimental system composed by an electro-hydraulic actuator is used. The actuator behaviour is dominated by a non-linear characteristic constituted by a deadzone and hysteresys effects. The paper shows experimental results describing the real-time controller capability in compensate the nonlinearities of the actuator and learn to improve its performance in trajectory following.