Nonlinear model predictive control of multivariable processes using block-structured models

Block-structured models, such as Wiener or Hammerstein models, have been used in nonlinear model predictive control to reduce the cost of identification and online computation. The solution of a nonlinear dynamic optimization problem has been avoided by inverting the nonlinear element and solving the resulting linear problem in the past. However, by exploiting the block structure for sensitivity calculation, the original nonlinear problem can also be solved at low computational cost. At the same time, greater modeling flexibility is achieved. Recently, a new Hammerstein model structure has been proposed for multivariable processes with input directionality, which exploits such increased modeling flexibility. This paper deals with nonlinear model predictive control constrained by models of Hammerstein or Uryson structure. A method for efficient calculation of sensitivity information is developed. In a simulation example, the method is shown to combine low computational cost with a significant reduction of the loss of optimality compared to the previous methods.