US 12,769,266 B2Granted

Stochastic control subject to generative AI-based disturbance

AssigneeMitsubishi Electric Research Laboratories, Inc.

InventorsAnkush Chakrabarty, Ye Wang, Christopher Laughman, Toshiaki Koike Akino, Gordon Wichern, Alessandro Salatiello, Farshud Sorourifar, Joel Paulson

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Abstract

The predictive controller determines, using the deep generative decoder model, a conditional probabilistic distribution of the latent representations of the disturbance conditioned on the partial observations of the disturbance, and samples the conditional probabilistic distribution of the latent representations to produce a latent sample of the time-series values of the disturbance affecting the mechanical system over the time horizon. The predictive controller decodes the latent sample with the deep generative decoder model to produce predicted values of the disturbance acting on the system within the time horizon with a probability of the latent sample on the conditional probabilistic distribution of the latent representations and controls the mechanical system using a predictive controller that determines control commands changing a state of the operation of the mechanical system using the probability of at least some of the predicted values of the disturbance.

Claims20

Dependent claims

A method for predictive control of an operation of a mechanical system subject to uncertainty of a disturbance acting on the mechanical system, wherein the method is using a processor coupled with stored instructions implementing steps of the method, comprising:

collecting partial observations of the disturbance affecting the operation of the mechanical system over an observed portion of a time horizon;
collecting a deep generative decoder model defining a mapping from a latent space of latent representations of time-series values of the disturbance affecting the mechanical system over the time horizon to a measurement space of the partial observations of the disturbance;
determining, using the deep generative decoder model, a conditional probabilistic distribution of the latent representations of the disturbance conditioned on the partial observations of the disturbance;
sampling the conditional probabilistic distribution of the latent representations to produce a latent sample of the time-series values of the disturbance affecting the mechanical system over the time horizon;
decoding the latent sample with the deep generative decoder model to produce predicted values of the disturbance acting on the system within the time horizon with a probability of the latent sample on the conditional probabilistic distribution of the latent representations;
determining control commands by a predictive controller based on the probability of at least some of the predicted values of the disturbance;
and
controlling the mechanical system by changing a state of the operation of the mechanical system based on the control commands generated by the predictive controller.

5 more elements

A device for predictive control of an operation of a mechanical system subject to uncertainty of a disturbance acting on the mechanical system comprising:

one or more processors configured to:
collect partial observations of the disturbance affecting the operation of the mechanical system over an observed portion of a time horizon;
collect a deep generative decoder model defining a mapping from a latent space of latent representations of time-series values of the disturbance affecting the mechanical system over the time horizon to a measurement space of the partial observations of the disturbance;
determine, using the deep generative decoder model, a conditional probabilistic distribution of the latent representations of the disturbance conditioned on the partial observations of the disturbance;
sample the conditional probabilistic distribution of the latent representations to produce a latent sample of the time-series values of the disturbance affecting the mechanical system over the time horizon;
decode the latent sample with the deep generative decoder model to produce predicted values of the disturbance acting on the system within the time horizon with a probability of the latent sample on the conditional probabilistic distribution of the latent representations;
determine control commands by a predictive controller based on the probability of at least some of the predicted values of the disturbance;
and
control the mechanical system by changing a state of the operation of the mechanical system based on the control commands generated by the predictive controller.

6 more elements

Dependencies are inferred from the claim text.