A method for controlling processing state of a plasma reactor, comprising: initiate processing of a substrate in the plasma reactor using settings for tuning knobs of the plasma reactor that are approximated to achieve desired processing state values; receiving a plurality of data streams from the plasma reactor during the processing of the substrate, the plurality of data streams used to identify current processing state values; generating a compensation vector, by a machine learning engine, that identifies differences between the current processing state values and the desired processing state values, wherein the desired processing state values and the current processing state values are defined in a virtual space that is descriptive of a physical state of plasma conditions sensed within a processing volume of the plasma reactor, the compensation vector is produced by said machine learning engine and said compensation vector is defined in said virtual space; transforming the compensation vector into adjustments to the settings for the tuning knobs; applying the adjustment to the tuning knobs of the plasma reactor; wherein the initiating processing includes: accessing a model database for a type of the plasma reactor and a type of a process to be used for the processing of the substrate in the plasma reactor; and identifying a model from the model database, the model includes the settings for tuning knobs of the plasma reactor that are approximated to achieve the desired processing state values, the desired processing state values from the model are defined in said virtual space and used as an input to said machine learning model. initiate processing of a substrate in the plasma reactor using settings for tuning knobs of the plasma reactor that are approximated to achieve desired processing state values; receiving a plurality of data streams from the plasma reactor during the processing of the substrate, the plurality of data streams used to identify current processing state values; generating a compensation vector, by a machine learning engine, that identifies differences between the current processing state values and the desired processing state values, wherein the desired processing state values and the current processing state values are defined in a virtual space that is descriptive of a physical state of plasma conditions sensed within a processing volume of the plasma reactor, the compensation vector is produced by said machine learning engine and said compensation vector is defined in said virtual space; transforming the compensation vector into adjustments to the settings for the tuning knobs; applying the adjustment to the tuning knobs of the plasma reactor; wherein the initiating processing includes: accessing a model database for a type of the plasma reactor and a type of a process to be used for the processing of the substrate in the plasma reactor; and identifying a model from the model database, the model includes the settings for tuning knobs of the plasma reactor that are approximated to achieve the desired processing state values, the desired processing state values from the model are defined in said virtual space and used as an input to said machine learning model. accessing a model database for a type of the plasma reactor and a type of a process to be used for the processing of the substrate in the plasma reactor; and identifying a model from the model database, the model includes the settings for tuning knobs of the plasma reactor that are approximated to achieve the desired processing state values, the desired processing state values from the model are defined in said virtual space and used as an input to said machine learning model.