An adaptive state tracking control scheme for T-S fuzzy models in non-canonical form and with uncertain parameters

This paper develops a novel adaptive state tracking control scheme based on Takagi-Sugeno (T-S) fuzzy models with unknown parameters. The proposed method can deal with T-S models in a non-canonical form and allows the number of inputs to be less than state variables, which is more practical and has wider applications. The needed matching conditions for state tracking are relaxed by using a T-S fuzzy reference model to generate desired state reference signals. A new fuzzy estimator model is constructed whose states are compared with those of the T-S fuzzy model to generate the estimator state error which is used for the parameter adaptive law. Based on the Lyapunov stability theory, it has been proven that all the signals in the closed-loop system are bounded and the asymptotic state tracking can be achieved. The effectiveness of the proposed scheme is demonstrated through a magnetic suspension system and a transport airplane model.