Robust MPC Design Using Orthonormal Basis Function for the Processes with ARMAX Model

Applying MPC in the case of rapid sampling, complicated process dynamics lead us to poorly numerically conditioned solutions and heavy computational load. Furthermore, there is always mismatch in a model that describes a real process. Therefore, in this paper in order to prevail over the mentioned difficulties, we design a MPC using Laguerre orthonormal basis functions based on ARMAX models. More precisely, the Laguerre function speed up the convergence at the same time with lower computation and ARMAX model guarantee's the offset free control adding the extra parameters "a" and "gamma" to MPC. The extra parameters as well as MPC parameters will be tuned in order to guarantee the robustness of the system against the model mismatch and measurement noise. Hence, in this novel MPC design the extra tuning parameters render a better closed loop performance since it explicitly balances the speed of convergence for the disturbance state and the sensitivity to noise in this estimate. The performance of the controller is examined controlling level of a Tank and Wood-Berry distillation column.