Controller
AssigneeKemira Oyj
InventorsTorsten Haverinen-Nielsen, Marjatta Piironen, Iiris Joensuu, Vesa-Matti Tikkala
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Abstract
The invention provides a control arrangement where the controller is arranged to drive the actuator utilizing automatically the explanation values. The control arrangement has a controller, which is arranged to drive an actuator. The control arrangement comprises also a setpoint controller, which is arranged to utilize deviations between explanation values of machine learning and normal explanation values of machine learning. The setpoint controller forms a setpoint value for the controller.
Claims12
A control arrangement having a controller, which is arranged to drive an actuator in an industrial process, the control arrangement comprising a setpoint controller arranged to utilize deviations between explanation values of machine learning and normal explanation values of machine learning, the deviations being input data to the setpoint controller, the setpoint controller forming a setpoint value for the controller, wherein the explanation values of machine learning and normal explanation values of machine learning are SHAP values, values from a LIME method, values from a DeepLIFT method or any other possible explanation values, wherein the setpoint controller is also arranged to utilize deviations between machine learning values and normal machine learning values, and/or deviations between measurement data and normal measurement data, and wherein said normal values are values that have been derived from good running periods of a process, which is controlled by the control arrangement, wherein the setpoint controller comprises at least one P module, an I module, or a D module, or any combination of these modules, said deviations being input data into said modules, the setpoint controller comprising also input mapping module/s for each output of said module/s, a summation module to sum output/s of the input mapping module/s, an output scaling module to scale an output of the summation module, an output mapping module in order to provide a normalized output, and a setpoint adjusting module utilizing the normalized output in order to change the setpoint value.
A method for controlling an industrial process using a control arrangement, the method comprising:
- forming a setpoint value for a controller of the control arrangement by determining deviations between explanation values of machine learning and normal explanation values of machine learning from a setpoint controller, the deviations being input data to the setpoint controller, wherein the explanation values of machine learning and normal explanation values of machine learning are SHAP values, values from a LIME method, values from a DeepLIFT method or any other possible explanation values, wherein the setpoint controller comprises at least one P module, an I module, or a D module, or any combination of these modules, said deviations being input data into said modules, the setpoint controller comprising also input mapping module/s for each output of said module/s, a summation module to sum output/s of the input mapping module/s, an output scaling module to scale an output of the summation module, an output mapping module in order to provide a normalized output, and a setpoint adjusting module utilizing the normalized output in order to change the setpoint value;
- determining deviations between machine learning values and normal machine learning values, and/or deviation between measurement data and normal measurement data using the setpoint controller, wherein said normal values are values that have been derived from good running periods of a process that has been controlled by the control arrangement;
- and
- driving an actuator of the control arrangement.
Dependencies are inferred from the claim text.