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, the repairing comprises steps of: (i) converting a set of user-defined RGA constraints into gain ratio constraints, (ii) solving a set of submodels of the updated master model, wherein each of the submodels includes gain ratio constraints, the solving modifying the set of submodels to satisfy the converted set of user-defined RGA constraints, and (iii) calculating an objective function over the set of submodels, and repeating the steps (i)-(iii) until the calculated objective function indicates RGA violations or general gain constraint violations are repaired in the updated master model; 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.