Generalized predictive control based on input design

Two drawbacks of the conventional generalized predictive control (GPC), the large amount of computation and the choice of the tuning parameter, are studied. The GPC based on input design (GPCID) is presented in which a new parameter is proposed, instead of the control weighting parameter, to impose constraints on control inputs. The parameter has a strong intuitive appeal to designers and is independent of the characteristics of controlled plants. The new algorithm alleviates the computational burden because it is not necessary to calculate the inverse matrix. Finally, the performance of the GPCID is compared to that of the conventional GPC on the control of a complex system. The simulation results show that it not only removes the above shortcomings of the conventional GPC, but has superior performance over the conventional GPC.