The original GMV self-tuner was later extended to provide a general framework which included feedforward compensation and user-chosen polynomials with detuned model-reference, optimal Smith predictor and load-disturbance tailoring objectives. This paper adds similar refinements to the GPC algorithm which are illustrated by a set of simulations. The relationship between GPC and LQ designs is investigated to show the computational advantage of the new approach. The roles of the output and control horizons are explored for processes with nonminimum-phase, unstable and variable dead-time models. The robustness of the GPC approach to model over- and under-parameterization and to fast sampling rates is demonstrated by further simulations. An appendix derives stability results showing that certain choices of control and output horizons in GPC lead to cheap LQ, “mean-level”, state-dead-beat and pole-placement controllers.