Method and Apparatus for Controlling a Plant Using Feedback Signals

• US 20110238189A1
• Filed: 03/26/2010
• Published: 09/29/2011
• Est. Priority Date: 03/26/2010
• Status: Abandoned Application

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Butera, William J., Yerazunis, William S.
• Application Number:
  US12/732,887
• Publication Number:
  US 20110238189A1
• Time in Patent Office: Days
• US Class Current: 700/30
• CPC Class Codes: G05B 19/41835 characterised by programme execution
• CPC Class Codes: G05B 2219/32015 Optimize, process management, optimize production line
• CPC Class Codes: G05B 2219/32018 Adapt process as function of results of quality measuring until maximum quality
• CPC Class Codes: G05B 2219/32019 Dynamic reconfiguration to maintain optimal design, fabrication, assembly
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

A method and apparatus controls a plant using feedback signals. A procedural description of a feedback control process for the plant is translated into a set of objects, wherein each object is a portion of the procedural description, wherein each object is a strictly-encapsulated and autonomous software module, wherein the objects execute in a platform, and wherein the platform includes a set of nodes embedded in the plant and each node includes a processor. A feedback signals is generated by passing messages between the set of the objects in response to an operation of the plant.

Claims

1. A method for controlling a plant using feedback signals, comprising the steps of:

translating a procedural description of a feedback control process for the plant into a set of objects, wherein each object is a portion of the procedural description, wherein each object is a strictly-encapsulated and autonomous software module, wherein the objects execute in a platform, and the platform includes a set of nodes embedded in the plant and each node includes a processor; and

generating the feedback signals by passing messages between the set of the objects in response to an operation of the plant.

2. The method of claim 1, further comprising:

measuring the operation of the plant with a set of sensors connected to the nodes;

sending the feedback signals to a set of actuators connected to the set nodes to control the operation of the plant.

3. The method of claim 1, wherein the plant is subject to dynamic physical phenomena, and a speed at which the feedback signals are generated is greater than a speed at which the phenomena propagate though the plant.

4. The method of claim 1, further comprising:

organizing the set of nodes into subsets of nodes, wherein a reference node each subset has direct communication links to all nodes in the subset.

5. The method of claim 1, wherein a degree-of-freedom (DOF) of the platform depends on a number of the nodes, an interconnect hierarchy, and a network topology, which includes node placement, node density, node regularity, and an interconnectivity configuration.

6. The method of claim 5, wherein the DOF includes mechanical DOF and information DOF.

7. The method of claim 1, wherein each node has a sub-cm² footprint, and the number of nodes is in a range 10¹ to 10⁶.

8. The method of claim 1, further comprising:

optimizing a performance of the platform and a distribution of the set of nodes according to a gradient descent cost function subject to constraints.

9. The method of claim 8, wherein the performance includes throughput, latency, memory size, power consumption, and redundancy.

10. The method of claim 1, further comprising:

adapting the feedback control process to the operation of the plant.

11. The method of claim 8, wherein an initial distribution of the objects over the nodes is random, and further comprising:

reconfiguring dynamically the objects in response to the operation of the plant.

12. The method of claim 11, wherein the reconfiguring is due to failure of any of the nodes, adding of nodes, removal of nodes, and changes in a computational capacity of any of the nodes.

13. The method of claim 8, further comprising:

distributing the a gradient descent cost function over the set of nodes.

14. The method of claim 1, wherein a spacing between the nodes is small compared to characteristic length scales of dynamic of the plant.

15. The method of claim 1, further comprising:

encoding the feedback control process according to process self-assembly (PSA) models.

16. An apparatus for controlling a plant using feedback signals, comprising:

a platform including a plurality of node, wherein the nodes are embedded in the plant and each node includes a processor;

means for translating a procedural description of a feedback control process for the plant into a set of objects, wherein each object is a portion of the procedural description, wherein each object is a strictly-encapsulated and autonomous software module, wherein the objects are configures to execute in the nodes; and

means for generating the feedback signals by passing messages between the set of the objects in response to an operation of the plant.

17. The apparatus of claim 16, further comprising:

a set of sensors configured to measure the operation of the plant; and

a set of actuators configured to process the feedback signals, wherein the set of sensors and the set of nodes are connected to the nodes.

18. The apparatus of claim 16, wherein the plant is subject to dynamic physical phenomena, and a speed at which the feedback signals are generated is greater than a speed at which the phenomena propagate though the plant.

19. The apparatus of claim 16, wherein the of nodes are organized into subsets of nodes, wherein a reference node each subset has direct communication links to all nodes in the subset.

20. The apparatus of claim 16, wherein each node has a sub-cm² footprint, and the number of nodes is in a range 10¹ to 10⁶.

21. The apparatus of claim 16, wherein a performance of the platform and a distribution of the set of nodes is optimized according to a gradient descent cost function subject to constraints.

22. The apparatus of claim 16, wherein an initial distribution of the objects over the nodes is random, and wherein the objects reconfigure dynamically in response to the operation of the plan.

23. The apparatus of claim 16, wherein a spacing of the nodes is relatively small compared to characteristic length scales of dynamics of the plant.

24. The apparatus of claim 16, wherein a performance of the platform scales up according to a number of the nodes.