High-gain observer-based adaptive fuzzy control for a class of multivariable nonlinear systems

We develop a fuzzy adaptive output-feedback control methodology for unknown nonlinear multivariable systems for which the input gains matrix is characterized by non-zero leading principle minors but not necessary symmetric. An high-gain (HG) observer is introduced to estimate the immeasurable states. A linear in parameters fuzzy system is adequately employed to model the uncertainties. A matrix factorization, so-called SDU, is used when designing the controller to factor the input gains matrix. An appropriate Lyapunov function is exploited to study the stability of the corresponding closed-loop control system as well as to derive the adaptation laws. A 2 DOF helicopter system is used to validate, in a simulation framework, the performances of our developed control approach.