Optimal control problems classification

Two main classes of optimization methods are available for handling uncertainty. The essential difference relates to whether or not measurements are used in the calculation of the optimal strategy. In the absence of measurements, a robust optimization approach is typically used, whereby conservatism is introduced to guarantee feasibility for the entire range of expected variations [18]. When measurements are available, adaptive optimization can help adjust to process changes and disturbances, thereby reducing conservatism [9]. It is interesting to note that the above classification is similar to that found in control problems with the robust and adaptive techniques. 

Using a classification algorithm we can determine the measured variables that are overmeasured, that is, the measurements that may also be obtained from mathematical relationships using other measured variables. In certain cases we are not interested in all of them, but rather in some that for some reason (control, optimization, reliability) are required to be known with good accuracy. On the other hand, there are unmeasured variables that are also required and whose intervals are composed of over measured parameters. Then we can state the following problem Select the set of measured variables that are to be corrected in order to improve the accuracy of the required measured and unmeasured process variables. 

Morari, M., Stephanopoulos, G. Arkun, Y. (1980), Studies in the synthesis of control structures for chemical processes, part 1 Formulation of the problem, process decomposition and the classification of the controller task, analysis of the optimizing cotnrol structures , AIChE Journal 26(2), 220-232. 

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