a new approach for pole placement of nonlinear systems using state feedback and fuzzy system is proposed. we use a new online fuzzy training method to identify and to obtain a fuzzy model for the unknown nonlinear system using only the system input and output. then, we linearized this identified model at each sampling time to have an approximate linear time varying system. in order to stabilize...

The design of stabilizing controllers for nonlinear plants with unknown nonlinearities is a challenging problem. The inability to identify exactly the nonlinearities on-line or off-line motivates the design of stabilizing controllers based on approximations or on approximate estimates of the plant nonlinearities. The price paid in such case, could be lack of theoretical guarantees for global st...

The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is diffic...

A new approach for pole placement of nonlinear systems using state feedback and fuzzy system is proposed. We use a new online fuzzy training method to identify and to obtain a fuzzy model for the unknown nonlinear system using only the system input and output. Then, we linearized this identified model at each sampling time to have an approximate linear time varying system. In order to stabilize...

A systematic investigation to derive nonlinear lattice equations governed by partial difference (P??E) admitting specific Lax representation is presented. Further it shown that for a value of the parameter derived P??E's can be transformed into linear under global transformation. Also demonstrated how higher order ordinary (O?E) or mappings in general and linearizable ones particular from obtai...

We consider an important class of dynamic singleinput single-output nonlinear systems where the system model is polynomial in observations but linear in parameters. The investigation is done in the errors-in-variables framework, i.e. both input and output are observed with noise. Assuming white Gaussian measurement noise that is characterized by a magnitude and a covariance structure, we propos...

Optimal linear controllers and high computational non-linear controllers are normally applied to control the nonlinear systems. Flatness control method is a control technique for linear systems as well as nonlinear systems by static and dynamic feedback namely as endogenous dynamic feedback. This method takes into account the non-linear behavior of the process while preventing complicated compu...

In this paper, taking the inverted pendulum as an example of nonlinear systems which are not exactly linearizable, we give a controller design method for the system based on the approximate linearization. In the method, we try to suppress the e ect of the higher order residual terms in choosing the new coordinate for the approximate linearization. Furthermore, we show its e ectiveness by experi...