Application of generalized predictive control to industrial processes

A novel algorithm called generalized predictive control (GPC) is shown to be particularly effective for the self-tuning control of industrial processes. The method uses long-range predictive control ideas with a carefully chosen controlled autoregressive and integrated moving average (CARMA) plant model and various horizons that allow for a rich variety of control objectives. The procedure can adapt to process dead time and model order, and a multivariable version gives tight control of complex plants without prior knowledge of the interactor matrix. Applications of GPC to a cement mill, a spray-drying tower, and a compliant robot arm give performance better than that of fully tuned proportional-integral-derivative regulators.<>