Optimal structures for steady-state adaptive optimizing control of large-scale industrial processes

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Abstract
Novel structures are developed and discussed for the steady-state optimizing control of large-scale industrial processes. A new class of hierarchical structures is proposed involving iterative procedures which utilize available process mathematical models and feedback information. In contrast to the majority of existing structures, the new techniques are optimal in the sense that the control produced by each structure satisfies the Kuhn-Tucker necessary optimality conditions. The structures are adaptive where the process model, which is assumed to be point-parametric, contains parameters which are updated at each iteration. Depending on the real process measurement capabilities, some of the structures incorporate output information feedback only, while others utilize both input and output measurements. These measurements are used in different ways leading to structures with local and global feedback, together with alternative coordination strategies.