System and method for implementing real-time applications based on stochastic compute time algorithms

• US 20040117042A1
• Filed: 12/17/2002
• Published: 06/17/2004
• Est. Priority Date: 12/17/2002
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Jackson, Warren B., Fromherz, Markus P.J.
• Granted Patent: US 6,941,177 B2
• Time in Patent Office: Days
• US Class Current: 700/51
• CPC Class Codes: G05B 13/024 in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

A method for developing and using real time applications for a dynamic system having a sensing subsystem, actuation subsystem, a control subsystem, and an application subsystem utilizes stochastic compute time algorithms. After optimization functions, desired state and constraints are received and detector data has been provided from a sensor subsystem, a statistical optimization error description is generated. From this statistical optimization error description a strategy is developed, including the optimization errors, within the control subsystem. An execution module within the control subsystem then sends an execution strategy to various actuators within the actuation subsystem.

Claims

1. A method for developing and using real time applications using stochastic compute time algorithms to control a dynamic system including a sensing subsystem, actuation subsystem, a control subsystem, and an application subsystem, comprising:

receiving initial detector data, optimization functions, desired state and constraints;

generating a statistical optimization error description;

developing a strategy including the optimization errors; and

executing said strategy in a real time application.

2. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein generating a statistical optimization error description further comprises updating said statistical description.

3. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein said statistical optimization error description is generated off-line.

4. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein said statistical optimization error description is generated during operation of the dynamic system.

5. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein generating said statistical optimization error description comprises optimizing a received optimization function, received constraints, and detector data over a fixed time budget and determining a probability distribution of optimization errors.

6. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein generating said statistical optimization error description comprises optimizing a received optimization function using received detector data over an unrestricted time budget and comparing to the optimization of said received optimization function using a restricted time budget to determine a probability distribution of errors.

7. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein generating said statistical optimization error description comprises receiving an actual system state and comparing said actual system state to the optimization of said received optimization function using a restricted time budget to determine a probability distribution of optimization errors.

8. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein generating said statistical description comprises:

optimizing said detector data over a fixed time budget;

optimizing said detector data over an unrestricted time budget; and

determining a probability distribution of errors.

9. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein generating a strategy comprises:

determining a maximum bound of errors from a probability distribution of solution errors;

using control error bounds as bounds for model uncertainty;

using state error bounds as bounds for observation uncertainty; and

determining stability and stability margins from error bounds.

10. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein generating a strategy comprises using the entire statistical distribution of the error as a measurement noise or disturbance to be rejected.

11. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein generating a strategy comprises using the statistical information in a Monte Carlo method, using sampling from the algorithm distribution to simulate the system behavior and evaluating the effect of a given control law and various changes to it to find an optimal control.

12. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein the application subsystem includes a control subsystem.

13. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein the application subsystem includes a diagnostic subsystem.

14. The method for developing and using real time applications using stochastic compute time algorithms according to claim 1, wherein the application subsystem includes a reconfiguration subsystem.

15. A dynamic system utilizing stochastic compute time algorithms in developing real time applications, the system comprising:

a sensing subsystem having a plurality of detectors;

a control subsystem including at least one system state module, at least one state action module, and at least one execution module;

communication means for transmitting output information from said plurality of detectors to said control subsystem;

at least one application module;

an actuation subsystem having a plurality of actuators; and

communication means for transmitting execution instructions from said execution module to said plurality of actuators.

16. The dynamic system utilizing stochastic compute time algorithms according to claim 15, wherein said applications module comprises a control subsystem.

17. The dynamic system utilizing stochastic compute time algorithms according to claim 15, wherein said applications module comprises a diagnosis subsystem.

18. The dynamic system utilizing stochastic compute time algorithms according to claim 15, wherein said application module comprises a reconfiguration subsystem.

19. The dynamic system utilizing stochastic compute time algorithms according to claim 15, wherein said system state module generates a statistical description of the dynamic system.

20. The dynamic system utilizing stochastic compute time algorithms according to claim 15, wherein in said system state module updates a statistical description of the dynamic system.

21. The dynamic system utilizing stochastic compute time algorithms according to claim 15, wherein said state action module generates a performance strategy.

22. An article of manufacture comprising a computer usable medium having computer readable program code embodied in said medium which, when said program code is executed by said computer causes said computer to perform method steps for developing and using real time applications using stochastic compute time algorithms on a dynamic system, said method comprising:

receiving initial detector data, optimization functions, desired system state, and constraints;

generating a statistical description;

developing a strategy; and

executing said strategy in a real time application.