Augmented model-based double iterative loop techniques for hierarchical control of complex industrial processes

Abstract

Two novel hierarchical structures are presented which extend the applicability of previous model-based double iterative loop techniques using input-output information feedback to non-convex problems. The techniques incorporate integrated system optimization and parameter estimation, which utilize process measurements to achieve real process optimality in spite of model reality differences. The double iterative loop structures of the proposed algorithms use the real process measurements within the output loops while the inner loops involve model-based computations only. By this means the algorithms use available information from the real process efficiently and a significant reduction in set-point alterations to real sub-processes is achieved. In order to cater for process complexity, the inner loops are organized in the form of two-level hierarchical structures. An applicability of the techniques to non-convex problems is achieved by a suitable convexification of an original process performance index. It ...