A self-tuning brain emotional learning-based intelligent controller for trajectory tracking of electrohydraulic actuator

摘要

Realization of biologically motivated algorithms in industrial applications is becoming a new research, especially in the field of electrohydraulic systems. One of the recent innovations named brain emotional learning-based intelligent controller has been catching eyes of the researcher as a model-free adaptive controller, which has effective capabilities to handle nonlinearities and uncertainties of controlled systems. The aim of this article is to develop a so-called self-tuning brain emotional learning-based intelligent controller for tracking control of electrohydraulic actuators. Here, the main control unit brain emotional learning-based intelligent controller is used to drive the system to desired targets. Meanwhile, a fuzzy inference is designed to tune online the reward function (RF) parameter of the brain emotional learning-based intelligent controller, which enables the system robustness and stability. A test rig employing an electrohydraulic actuator is then setup to investigate the system control performance. The experimental results implied that proposed controller has strong ability to drive the system to follow different reference trajectories with minimal errors.