Reference compensation technique of a fuzzy controlled inverted pendulum system for control education

This article presents the implementation of a neuro-fuzzy like controller design for an inverted pendulum system. The inverted pendulum system is controlled by a nominal Takagi-Sugeno-Kang (TSK) type fuzzy controller whose outputs are linear. Then the neural network is added to improve system performances by compensating signals at the reference input. Shaping input signals forms an inverse dynamics control scheme of the closed loop system whose scheme is called the reference compensation technique. The back-propagation learning algorithm for the neural network is derived for on-line learning and control. The learning algorithm has been implemented on a DSP 6713 board to achieve real time control. The proposed controller has been tested to control both balancing and tracking the position of the inverted pendulum system.