Physics informed neural network for learning non-Euclidean dynamics in electro-mechanical systems for synthesizing energy-based controllers
AssigneeSiemens Aktiengesellschaft
InventorsBiswadip Dey, Yaofeng Zhong, Amit Chakraborty
Life of this patent
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Abstract
System and method for synthesizing a controller for a dynamical system includes a feeder neural network trained to estimate an ordinary differential equation (ODE) from time series training data (X) of a trajectory having embedded angular data and configured to learn dynamics of a physical system by encoding a generalization of a Hamiltonian representation of the dynamics using a constant external control term (u). A neural ODE solver receives the estimate of the ODE from the feeder neural network and synthesizes a controller to control the system to track a reference configuration.
Claims12
A system of neural networks for synthesizing a controller, comprising:
- a processor;
- and
- a non-transitory memory having stored thereon modules executed by the processor, the modules comprising:
- a feeder neural network trained to estimate an ordinary differential equation (ODE) from time series training data (X) of a trajectory of a robot having a combination of embedded angular data and generalized coordinate and momentum data, configured to learn dynamics of a physical system that includes the robot by encoding a generalization of a Hamiltonian representation of the dynamics using a constant external control term (u) for driving parameter learning in the feeder neural network;
- and
- a neural ordinary differential equation (ODE) solver configured to receive the estimate of the ODE from the feeder neural network, and to synthesize a controller to control the system to track a reference configuration, wherein the controller is configured to control the robot based on the reference configuration that is tracked.
A method for synthesizing a controller, comprising:
- using a feeder neural network trained to estimate an ordinary differential equation (ODE) from time series training data (X) of a trajectory of a robot having a combination of embedded angular data and generalized coordinate and momentum data;
- learning dynamics of a physical system that includes the robot by encoding a generalization of a Hamiltonian representation of the dynamics using a constant external control term (u) for driving parameter learning in the feeder neural network;
- receiving the estimate of the ODE from the feeder neural network;
- and
- synthesizing a controller to control the system to track a reference configuration;
- and
- controlling the robot based on the reference configuration that is tracked.
Dependencies are inferred from the claim text.