1. A computer implemented method of model predictive control (MPC) of a subject dynamic process by a MPC controller, the MPC controller representing the subject dynamic process by a master model, the method comprising: receiving a set of model structure information as input by a user, the set of model structure information comprising a plurality of constraints representative of engineering judgment; receiving a set of process data from each cycle of the subject dynamic process; creating a set of cases from the master model, the cases comprising information about the subject dynamic process, and each case being a portion of the master model; specifying a set of user-selected constraints from the model structure information and the master model; from one or more cases of the set of cases, creating a plurality of linear dynamic models of the subject dynamic process using the set of user-selected constraints, the set of process data, and a model identification calculation; creating an updated master model using the plurality of linear dynamic models and a coordinating optimum model calculation; repairing collinearity in the updated master model using at least one of: gain uncertainty or RGA uncertainty; and implementing the updated master model to the MPC controller, wherein implementing the updated master model preserves the plurality of constraints representative of engineering judgment.