Abstract
In many applications, e.g., in chemical process control, the purpose of control is to achieve an optimal performance of the controlled system despite the presence of significant uncertainties about its behavior and of external disturbances. Tracking of set points is often required for lower-level control loops, but at the system level in most cases, this is not the primary concern and may even be counterproductive. In this entry, the use of dynamic online optimization on a moving finite horizon to realize optimal system performance is discussed. By real-time optimization, a performance-oriented or economic cost criterion is minimized or maximized over a finite horizon while the usual control specifications enter as constraints but not as set points. This approach integrates the computation of optimal set-point trajectories and of the regulation to these trajectories.
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Engell, S. (2015). Model-Based Performance Optimizing Control. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5058-9_244
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DOI: https://doi.org/10.1007/978-1-4471-5058-9_244
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