INTERNAL MODEL-PREDICTIVE CONTROL (IMPC)

A computationally simple model predictive control algorithm incorporates the attractive features of the internal model control (IMC) law. The algorithm first computes the IMC control effort via a model state feedback implementation that automatically compensates for past control effort saturation. Before applying the calculated control, the algorithm checks to see if this control effort, when applied over a single sampling interval and followed by a control effort at the opposite limit (relative to its steady-state level), will cause the model output to exceed its desired trajectory. If not, the calculated control is applied. Otherwise the control is reduced appropriately. Application of the new algorithm to a variety of linear single-input single-output systems shows a smooth, rapid response, without significant overshoot. Comparisons with a QDMC algorithm, tuned to give the same unconstrained behavior as the IMC system and the best possible constrained performance, favor the IMPC system. Application of the new algorithm to a simple multivariable problem drawn from web control in film manufacturing demonstrates the flexibility of the algorithm in dealing with control effort saturation in multivariable systems.