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Industrial process control systems try to keep an output variable within agiven tolerance around a target value. PID control systems have been widelyused in industry to control input variables in order to reach this goal.However, this kind of Transfer Function based approach cannot be extended tocomplex processes where input data might be non-numeric, high dimensional,sparse, etc. In such cases, there is still a need for determining the subspaceof input data that produces an output within a given range. This paper presentsa non-stochastic heuristic to determine input values for a mathematicalfunction or trained regression model given an output range. The proposed methodcreates a synthetic training data set of input combinations with a class labelthat indicates whether the output is within the given target range or not.Then, a decision tree classifier is used to determine the subspace of inputdata of interest. This method is more general than a traditional controller asthe target range for the output does not have to be centered around a referencevalue and it can be applied given a regression model of the output variable,which may have categorical variables as inputs and may be high dimensional,sparse... The proposed heuristic is validated with a proof of concept on a realuse case where the quality of a lamination factory is established to identifythe suitable subspace of production variable values.

Determining input variable ranges in Industry 4.0: A heuristic for  estimating the domain of a real-valued function or trained regression model  given an output range