Adaptation to unmeasured variables

• US 20010014834A1
• Filed: 12/14/2000
• Published: 08/16/2001
• Est. Priority Date: 01/08/1999
• Status: Active Grant

• Current Assignee: Tokyo Electron Limited
• Original Assignee: Tokyo Electron Limited
• Inventors: Shah, Sunil C.
• Granted Patent: US 6,745,087 B2
• Time in Patent Office: Days
• US Class Current: 700/29
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G11B 20/00 Signal processing not specific to the method of recording or reproducing; Circuits therefor

Abstract

A scheme for management of uncertainty in control systems that require adaptation is described. The scheme removes as much uncertainty as possible at design time and manufacturing time by taking advantage of available compute resources in pre-computing a large set of robustness-performance tradeoff based model controllers at different operating conditions for different values of uncertainty parameters that occur in-use and in manufacturing. A group of control models, and corresponding model controllers with an uncertainty bound larger than the best-tuned controller, are generated. A subset of the model controllers is implemented in the system at manufacturing time based on characterization of the system. The subset of model controllers is switched at run time based on transient information received during operation of the system.

Claims

1. A method of generating a control system, comprising:

generating a plurality of control models of the plant based on an operation of a prototype at a design time;

characterizing an operation of the plant at a manufacturing time; and

selecting a plurality of model controllers based on the characterizing of the operation of the plant at manufacturing time, each of the plurality of model controllers corresponding to one of the plurality of control models.

2. The method of

claim 1

, further comprising selecting one of the plurality of model controllers at a run time for the plant based on an operating parameter.

3. The method of

claim 1

, wherein the plurality of control models includes an input-output model, disturbance output stochastic model, and quasi-periodic disturbances.

4. The method of

claim 1

, wherein generating the plurality of control models comprises pre-computing a plurality of model controllers based on robust-performance design based model controllers for different plant conditions that occur in-use and in-manufacturing of the plant.

5. The method of

claim 4

, wherein generating the plurality of control models at the design time further comprises developing an uncertainty model.

6. The method of

claim 5

, wherein developing an uncertainty model comprises:

obtaining a nominal model under a specific operating condition based on the operation of the prototype; and

obtaining a plurality of model variations of the prototype.

7. The method of

claim 6

, wherein developing an uncertainty model further comprises choosing a metric to represent the plurality of model variations.

8. The method of

claim 7

, wherein the metric is defined in terms weighted frequency responses.

9. The method of

claim 8

, wherein the metric reflects sensitivity of closed-loop performance to variation in a plant model.

10. The method of

claim 5

, wherein the plurality of control models are excited with trajectories in a frequency range, each of the plurality of control models designed to function in a portion of the frequency range.

11. The method of

claim 2

, wherein characterizing the operation of the plant comprises measuring unit-to-unit variations and in-unit variations in the operation of the plant.

12. The method of

claim 11

, further comprising implementing the plurality of model controllers in a system controller.

13. The method of

claim 11

, wherein the plurality of model controllers are hardware implemented in the system controller.

14. The method of

claim 11

, wherein the plurality of model controllers are software implemented in the system controller.

15. A controller, comprising:

a plurality of model controllers, each of the plurality of model controllers coupled to receive a plurality of monitored signals, each of the plurality of model controllers to generate a control signal; and

a data evaluator coupled to receive the plurality of monitored signals and generate an output, the data evaluator to determine when sufficient information from the plurality of monitored signals is received, the data evaluator coupled to select the control signal of one of the plurality of model controllers based on the output of the data evaluator.

16. The controller of

claim 15

, wherein the data evaluator comprises a model hypothesis tester and test confidence indicator.

17. The controller of

claim 16

, wherein the test confidence indicator utilizes a generalized likelihood ratio test to determine when sufficient information from the plurality of monitored signals is received.

18. The controller of

claim 16

, further comprising a fall-back controller coupled to receive the plurality of monitored signals, the fall-back controller generating the control signal and wherein the data evaluator is coupled to select the control signal of the fall-back controller when insufficient information from the plurality of monitored signals is received.

19. The controller of

claim 18

, further comprising a controller selector coupled to receive the output of the data evaluator, the controller selector coupled to receive the control signal of each of the plurality of model controllers and the fall-back controller, the controller selector to output the control signal of one of the plurality of model controllers and the fall-back controller based on output of the data evaluator.

20. The controller of

claim 19

, wherein the plurality of model controllers have adaptive parameters.

21. The controller of

claim 20

, wherein the parameters of the plurality of model controllers are gradually adjusted.

22. The controller of

claim 21

, wherein plurality of monitored signals are transients in a head position signal in a disk drive system and wherein the control signal adjusts a current in a motor in the disk drive system.

23. A method of controlling a plant, comprising:

receiving a plurality of monitored signals by a plurality of model controllers and a data evaluator;

evaluating the plurality of monitored signals to determine when sufficient information of the plurality of monitored signals is received; and

selecting one of the plurality of model controllers to generate a control signal based on the plurality of monitored signals.

24. The method of

claim 23

, wherein each of the plurality of the model controllers have adaptive parameters affecting the generated control signal and wherein the method further comprises gradually adjusting the adaptive parameters of the selected one of the plurality of model controllers.

25. The method of

claim 23

, wherein evaluating comprises performing a generalized likelihood ratio test on the plurality of monitored signals to determine which of the plurality of model controllers to select.

26. A controller, comprising:

a plurality of model controllers, each of the plurality of model controllers coupled to receive a plurality of monitored signals, each of the plurality of model controllers to generate a control signal; and

a data evaluator coupled to receive the plurality of monitored signals and generate an output, the data evaluator to determine when sufficient information from the plurality of monitored signals is received, the data evaluator coupled to select the control signal of one of the plurality of model controllers based on a generalized likelihood ratio test with frequency weighting to reflect closed-loop performance sensitivity to variations in a plant model.

27. The controller of

claim 26

, wherein the data evaluator comprises a test confidence indicator to delay switching among the plurality of model controllers until confidence in a hypothesis test is high.

28. The controller of

claim 27

, wherein the confidence is based on a number of seek settles in a given time.

29. The controller of

claim 27

, wherein the confidence is based on an average input power being high.

30. The controller of

claim 27

, wherein the confidence is based on a difference in log-likelihood ratio estimates.

31. The controller of

claim 27

, further comprising a fall-back controller coupled to receive the plurality of monitored signals, the fall-back controller generating the control signal and wherein the data evaluator is coupled to select the control signal of the fall-back controller when confidence in the hypothesis test is low.

32. A disk drive system, comprising:

a head assembly; and

a servo controller coupled to the head assembly, the servo controller comprising;

a plurality of model controllers, each of the plurality of model controllers coupled to receive a plurality of head position sensor signals, each of the plurality of model controllers to generate a control signal; and

a model hypothesis tester and test confidence indicator coupled to receive the plurality of head position sensor signals and generate a model index, the model hypothesis tester and test confidence indicator to determine when sufficient information from the plurality of head position sensor signals is received, the model hypothesis tester and test confidence indicator coupled to select the control signal of one of the plurality of model controllers based on the model index.

33. The disk drive system of

claim 32

, wherein the servo controller further comprises:

a fall-back controller coupled to receive the plurality of head position sensor signals, the fall-back controller generating the control signal and wherein the model hypothesis tester and test confidence indicator is coupled to select the control signal of the fall-back controller when insufficient information from the plurality of head position sensor signals is received; and

a controller selector coupled to receive the model index, the controller selector coupled to receive the control signal of each of the plurality of model controllers and the fall-back controller, the controller selector to output the control signal of one of the plurality of model controllers based on the model index.

34. The disk drive system of

claim 33

, wherein the control signal is a voice coil motor current.

35. The disk drive system of

claim 33

, wherein the control signal is an actuator voltage.

36. A method of handling disk drive variations, comprising:

receiving a plurality of position sensor signals and a control signal by a servo controller, the plurality of position sensor signals having a range of a factor;

evaluating the plurality of position sensor signals to determine when sufficient information of the plurality of position sensor signals is received; and

selecting one of a plurality of model controllers within the servo controller to operate on the control signal based on the factor.

37. The method of

claim 36

, wherein evaluating comprises performing a generalized likelihood ratio test on the plurality of position sensor signals to determine when there is a high confidence in the plurality of position sensor signals received by the servo controller.

38. The method of

claim 37

, wherein each of the plurality of the model controllers have adaptive parameters affecting the received control signal and wherein the method further comprises gradually adjusting the adaptive parameters of the selected one of the plurality of model controllers.

39. The method of

claim 36

, wherein the factor is resonance.

40. The method of

claim 36

, wherein the factor is disturbance.

41. The method of

claim 40

, wherein the factor is environment.

42. A method of designing control models, comprising:

manufacturing a first prototype of a system;

characterizing the first prototype of the system to obtain a first data; and

computing a plurality of control models based on the first data of the system.

43. The method of

claim 42

, further comprising:

generating a computational simulation model of the system;

operating the simulation model to obtain a second data of the system; and

computing the plurality of control models based on the first data and the second data of the system.

44. The method of

claim 43

, further comprising:

manufacturing a plurality of prototypes of the system;

characterizing manufacturing variations of the plurality of prototypes of the system to obtain a third data; and

computing the plurality of control models based on the first data, the second data, and the third data.

45. The method of

claim 43

, further comprising performing a plurality of iterations of refining the simulation model and computing the plurality of control models.

46. The method of

claim 45

, wherein computing the plurality of control models comprises exciting the plurality of control models with trajectories at an operating condition, each of the plurality of control models designed to function at the operating condition.

47. A method of manufacturing disk drives, comprising:

designing a plurality of control models and a corresponding plurality of uncertainty models of a servo system under at least one operating condition;

manufacturing a disk drive; and

characterizing the disk drive to obtain a nominal control model from the plurality of control models.

48. The method of

claim 47

, further comprising implementing a plurality of model controllers corresponding to a set of the plurality of control models in a controller of the servo system based on the nominal control model.

49. The method of

claim 48

, further comprising selecting one of the plurality of model controllers during operation of the disk drive.

50. The method of

claim 48

, wherein the at least one operating condition is a condition selected from a group consisting of temperature, humidity, vibration spectrum, and age.