Siemens Aktiengesellschaft

Exxonmobil Research and Engineering Company

Siemens Aktiengesell Schaft

Max A Fahrenkopf

Juan L Aparicio Ojea

Chengtao Wen

William P Snow

Ivan E Rodriguez Colon

For manufacturing process control, closed-loop control is provided () based on a constrained reinforcement learned network (). The reinforcement is constrained () to account for the manufacturing application. The constraints may be for an amount of change, limits, or other factors reflecting capabilities of the controlled device and/or safety.

Manufacturing process control using constrained reinforcement machine learning

Systems and methods are provided for interfacing multiple layers of optimization for a model of one or more processes in a processing environment to achieve increased or maximized stability in the underlying layer. To improve consistency between the solutions at the different model levels, the lower level of optimization can have extra constraints added to the optimization problem which target variables at their unconstrained values in the upper layer of optimization. The systems and methods can facilitate selection of variables to receive an external target such that stability of the solution is improved or maximized. This can be achieved, at least in part, by identifying variables that provide a reduced or minimized condition number for a sub-matrix in the lower level model when an additional external constraint is applied. The sub-matrix with the reduced or minimized condition number can correspond to a partitioned portion of the gain matrix that corresponds to unconstrained independent variables and constrained dependent variables.

Interface between processing environment optimization layers

Systems and methods for automatic generation of robot control policies include a CAD-based simulation engine for simulating CAD-based trajectories for the robot based on cost function parameters, a demonstration module configured to record demonstrative trajectories of the robot, an optimization engine for optimizing a ratio of CAD-based trajectories to demonstrative trajectories based on computation resource limits, a cost learning module for learning cost functions by adjusting the cost function parameters using a minimized divergence between probability distribution of CAD-based trajectories and demonstrative trajectories; and a deep inverse reinforcement learning engine for generating robot control policies based on the learned cost functions.

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Palo Alto, CA, United States of America

Max A Fahrenkopf