Properties of Generalized Predictive Control
1987; Elsevier BV; Volume: 20; Issue: 5 Linguagem: Inglês
10.1016/s1474-6670(17)55480-4
ISSN2589-3653
Autores Tópico(s)Control Systems and Identification
ResumoControl design using long-range prediction based on a dynamic model of the plant has become an important contender for high-performance applications. The model can be derived analytically, but more typically it is provided by simple experiments, or in the adaptive context by recursive estimation. Many methods have been proposed in the literature depending on the assumed model structure and the choice of cost-functions these are reviewed and an approach which embraces their best features - called Generalized Predictive Control or GPC - is presented. Of special interest is the demonstration that the selection of particular 'horizons' (the 'costing horizons' and the' 'control horizon') leads to well-understood basic techniques such as dead-beat, pole-placement, Generalized Minimum Variance and LQ. Simulated examples show that GPC is suitable for controlling complex plant such as unstable/inverse unstable systems. Preprogrammed set-points (аs with robot trajectory control) and actuator nonlinearities can also be catered for. A discussion of adaptive applications of GPC to several industrial processes concludes that the method is easy to use and effective.
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