adaptive fuzzy pole placement for stabilization of non-linear systems

A new approach is proposed for the pole placement of non-linear systems using the state feedback and fuzzy system. we use a new online fuzzy training method in order to identify and obtain a fuzzy model for an unknown non-linear system using only the system input and output. then we linearize this identified model at each sampling time to have an approximate linear time-varying system. in order to stabilize the linear system obtained, we first choose the desired time-invariant closed-loop matrix, and then a time-varying state feedback is used. the behavior of the closed-loop non-linear system is regarded as a linear time-invariant (lti) system. therefore, the advantage of the proposed method is the global asymptotical exponential stability of unknown non-linear systems. due to the high speed convergence of the proposed adaptive fuzzy training method, the closed-loop system is robust against uncertainty in system parameters. finally, a comparison is made with the boundary layer sliding mode control (smc).