Fuzzy optimal control using generalized Takagi-Sugeno model for multivariable nonlinear systems

In this work, the main objective is to obtain enhanced performance of nonlinear multivariable systems. Several algorithms of Fuzzy Logic Controller based Linear Quadratic Regulator (FLC-LQR) are presented. The multivariable nonlinear system is represented by a generalized Takagi¿Sugeno (T¿S) model developed by the authors in previous works. This model has been improved using the well known weighting parameters approach to optimize local and global approximation. In comparison with existent works, the proposed controller is based on the calculation of the control action in each point of the state space according to the dynamic properties of the nonlinear system at that point. This control methodology offers a robust, well damped dynamic response and zero steady state error when the system is subjected to disturbances and modeling errors. A two-link robot system is chosen to evaluate the robustness of the proposed controller algorithms.This work is funded by Spanish Ministry of Innovation and Science (ARABOT project DPI2010-21247-C02-01)Peer Reviewe