Model predictive control with state estimation and adaptation mechanism for a continuous stirred tank reactor

Two predictive controllers have been designed in this paper. The first model predictive controller is designed by considering a state space model and an extended kalman filter for estimate of the states of nonlinear model. The second one is based on the linear ARMA model and by employing adaptation mechanism; it can be applied to the nonlinear systems. Identification of the linear model parameters in each sample time from a recursive least square method is the suggested technique for adaptation. These methods are applied to a CSTR; as a nonlinear MIMO system with considering measurable disturbances. Simulations are performed for normal operating condition and a case in which system is caused with disturbance.