Performance enhancement for T-S fuzzy control using neural networks
A new control scheme is proposed to improve the system performance for Takagi-Sugeno (T-S) fuzzy system using control grade functions tuned by neural networks. First, systematic modeling method is introduced to construct the exact T-S fuzzy model for a nonlinear control system. For the T-S fuzzy model, the system uncertainty affects only the membership functions. To cope with this problem, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of linear matrix inequalities (LMIs) which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme is applied to a ball-and-beam system example verified by numerical simulations.
neural networks takagi-sugeno (t-s) fuzzy system fuzzy modeling linear matrix inequalities (lmis)