Adaptive control system capable of recovering from unexpected situations
First Claim
1. A system for controlling a vehicle using a set of competing adaptable predictive models of the dynamics of the vehicle, the system comprising:
- one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of;
using each competing adaptable predictive model, generating a set of predicted sensory data based on prior sensory data and prior vehicle control inputs, each competing adaptable predictive model comprising a neural network;
comparing each set of predicted sensory data with subsequent sensory data collected from the vehicle;
using the comparison of each set of predicted sensory data, automatically identifying one of the competing adaptable predictive models as a match;
if a match is identified,modifying the adaptable predictive model that is the match according to a second set of sensory data, andcontrolling the vehicle using control outputs generated using the match; and
if a match is not identified,generating a new adaptable predictive model through automatic model learning using the second set of sensory data, andcontrolling the vehicle using control outputs generated using the new adaptable predictive model.
1 Assignment
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Accused Products
Abstract
Described is a system for generating an adaptable predictive model of the dynamics of a complex system, such as a vehicle. A set of current sensory data describing the vehicle'"'"'s current state is collected from the vehicle. A set of predictive models is evaluated to identify a predictive model that best matches the vehicle'"'"'s current state. If a predictive model is identified as a match, then the predictive model is adapted to better fit the vehicle'"'"'s current state, resulting in an adapted predictive model. If a match is not identified, then a new predictive model is generated through automatic model learning using the current set of sensory data. The adapted predictive model or the new predictive model is then used to generate control outputs for the vehicle.
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Citations
24 Claims
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1. A system for controlling a vehicle using a set of competing adaptable predictive models of the dynamics of the vehicle, the system comprising:
one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of; using each competing adaptable predictive model, generating a set of predicted sensory data based on prior sensory data and prior vehicle control inputs, each competing adaptable predictive model comprising a neural network; comparing each set of predicted sensory data with subsequent sensory data collected from the vehicle; using the comparison of each set of predicted sensory data, automatically identifying one of the competing adaptable predictive models as a match; if a match is identified, modifying the adaptable predictive model that is the match according to a second set of sensory data, and controlling the vehicle using control outputs generated using the match; and if a match is not identified, generating a new adaptable predictive model through automatic model learning using the second set of sensory data, and controlling the vehicle using control outputs generated using the new adaptable predictive model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented method for controlling a vehicle using a set of competing adaptable predictive models of the dynamics of the vehicle, comprising:
an act of causing a data processor to execute instructions stored on a non-transitory memory such that upon execution, one or more processors perform operations of; using each competing adaptable predictive model, generating a set of predicted sensory data based on prior sensory data and prior vehicle control inputs, each competing adaptable predictive model comprising a neural network; comparing each set of predicted sensory data with subsequent sensory data collected from the vehicle; using the comparison of each set of predicted sensory data, automatically identifying one of the competing adaptable predictive models as a match; if a match is identified, modifying the adaptable predictive model that is the match according to a second set of sensory data, and controlling the vehicle using control outputs generated using the match; and if a match is not identified, generating a new adaptable predictive model through automatic model learning using the second set of sensory data, and controlling the vehicle using control outputs generated using the new adaptable predictive model. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A computer program product for controlling a vehicle using a set of competing adaptable predictive models of the dynamics of the vehicle, the computer program product comprising computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having a processor for causing the processor to perform operations of:
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using each competing adaptable predictive model, generating a set of predicted sensory data based on prior sensory data and prior vehicle control inputs, each competing adaptable predictive model comprising a neural network; comparing each set of predicted sensory data with subsequent sensory data collected from the vehicle; using the comparison of each set of predicted sensory data, automatically identifying one of the competing adaptable predictive models as a match; if a match is identified, modifying the adaptable predictive model that is the match according to a second set of sensory data, and controlling the vehicle using control outputs generated using the match; and if a match is not identified, generating a new adaptable predictive model through automatic model learning using the second set of sensory data, and controlling the vehicle using control outputs generated using the new adaptable predictive model. - View Dependent Claims (20, 21, 22, 23, 24)
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Specification