Optimal Control Based on Fuzzy Estimation of Takagi-Sugeno Model for the Furuta Pendulum: Experimental Results

The aim of this paper is an experimental study of a discrete-time fuzzy optimal controller based on blending of Takagi-Sugeno (T-S) fuzzy modeling and the Linear Quadratic Regulator (LQR) for an Underactuated Mechanical System. The proposed scheme combines the optimaUty of the LQR in terms of a desired behavior dynamic of the system with admissible control actions and, the approximation capability of nonlinear functions of T-S fuzzy model. From an input-output data a T-S fuzzy model based on an improved approach of fuzzy identification is estimated and, an extended optimal state feedback control is used in order to control stabilization and to guarantee reference tracking of Furuta's Pendulum. For the purpose of validating the controller scheme proposed experimental tests on QNET Rotary Inverted Pendulum Trainer for NI ELVIS are carried out. The performance of designed controller is compared against the classical LQR using an Integral Square Error (ISE) index.