GENERALIZED PREDICTIVE CONTROL .1. THE BASIC ALGORITHM

Current self-tuning algorithms lack robustness to prior choices of either dead-time or model order. A novel method - generalized predictive control or GPC - is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement. This receding-horizon method depends on predicting the plant's output over several steps based on assumptions about future control actions. One assumption - that there is a 'control horizon' beyond which all control increments become zero - is shown to be beneficial both in terms of robustness and for providing simplified calculations. GPC can be used either to control a 'simple' plant (e. g. open-loop stable) with little prior knowledge or a more complex plant such as nonminimum-phase, open-loop unstable and having variable dead-time. In particular GPC seems to be unaffected (unlike pole-placement strategies) if the plant model is overparameterized. Furthermore, as offsets are eliminated by the consequence of assuming a CARIMA plant model, GPC is a contender for general self-tuning applications.