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11/05/2009NASA Grant URC NCC NNX08BA44A1 Control Team Faculty Advisors Dr. Helen Boussalis Dr. Charles Liu Student Assistants Jessica Alvarenga Danny Covarrubias.

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Presentation on theme: "11/05/2009NASA Grant URC NCC NNX08BA44A1 Control Team Faculty Advisors Dr. Helen Boussalis Dr. Charles Liu Student Assistants Jessica Alvarenga Danny Covarrubias."— Presentation transcript:

1 11/05/2009NASA Grant URC NCC NNX08BA44A1 Control Team Faculty Advisors Dr. Helen Boussalis Dr. Charles Liu Student Assistants Jessica Alvarenga Danny Covarrubias Alinn Herrera Alfie Gil David Bogdanchik Antonio Martinez

2 Research Areas Air-breathing Hypersonic Flight Vehicle (AHFV) Testbed Research: –System Performance Analysis –State Estimation Methods 11/05/2009NASA Grant URC NCC NNX08BA44A2

3 Air-breathing Hypersonic Flight Vehicle (AHFV) Alfie Gil Antonio Martinez 11/05/20093NASA Grant URC NCC NNX08BA44A

4 Testbed Research 11/05/20094NASA Grant URC NCC NNX08BA44A

5 Outline Project Background Testbed Overview Panel Subsystem Current Research 11/05/20095NASA Grant URC NCC NNX08BA44A

6 Hubble Space Telescope 11/05/20096NASA Grant URC NCC NNX08BA44A

7 Hubble Space Telescope Project Background James Webb Space Telescope 11/05/20097NASA Grant URC NCC NNX08BA44A

8 11/05/20098NASA Grant URC NCC NNX08BA44A Ariane 5ECA

9 JWST Orbit 11/05/20099NASA Grant URC NCC NNX08BA44A

10 Testbed Diagram 11/05/200910NASA Grant URC NCC NNX08BA44A 1 2 3 4 5 6 C

11 Optical Scoring Systems 11/05/200911NASA Grant URC NCC NNX08BA44A http://www.calstatela.edu/orgs/space/optoscoresys.htm

12 Performance Analysis David Bogdanchik 11/05/200912NASA Grant URC NCC NNX08BA44A

13 Purpose To analyzing testbed performance when one or more sensors fail The performance measured is how well the telescope can maintain its parabolic shape Since the telescope will not be serviceable, it is important to know how well it will respond to sensor failure The number and magnitude of faults the controller can handle before the testbed shaping accuracy is compromised will be studied. 11/05/2009 NASA Grant URC NCC NNX08BA44A 13

14 Previous Progress Learned the basics of C, Matlab and LabVIEW Learned how to run the testbed and collect data Had been trying several different C programs out to see which would be best to fault a sensor 11/05/200914NASA Grant URC NCC NNX08BA44A

15 Objective Start with one panel (subsystem) Implement a combination of sensor faults through a C program (zerosensor3) Observe controller performance using LabVIEW for Data Acquisition Use Matlab to analyze results 11/05/200915NASA Grant URC NCC NNX08BA44A

16 Zerosensor3 Program Inputs sent to actuators by controller Sensor(s) failed by program Controller responds to this failure, and compensates for it CONTROLLER ACTUATOR PLANT SENSOR 11/05/200916NASA Grant URC NCC NNX08BA44A

17 LabVIEW GUI Program 11/05/200917NASA Grant URC NCC NNX08BA44A

18 No Fault Test 1 Test 2 11/05/200918NASA Grant URC NCC NNX08BA44A

19 Fault (Step Input of 5) Test 1 Test 2 11/05/200919NASA Grant URC NCC NNX08BA44A

20 Fault (Step Input of 10) Test 1 Test 2 11/05/200920NASA Grant URC NCC NNX08BA44A

21 Comparison No Fault Fault (Step 5) Fault (Step 10) 11/05/200921NASA Grant URC NCC NNX08BA44A

22 Future Work Figure out how the controller compensates for different faults Determine why some of the sensors read with more disturbance Implement tests with numerous fault combinations Run the tests on two panels at a time 11/05/200922NASA Grant URC NCC NNX08BA44A

23 State Estimation Methods: Observer and Kalman Filter Alinn Herrera Jessica Alvarenga 11/05/200923NASA Grant URC NCC NNX08BA44A

24 Outline Problem Recap Previous Goals Kalman Filter Simulation Noise Modeling Future goals 11/05/2009NASA Grant URC NCC NNX08BA44A24

25 Problem Fault Detection Isolation (FDI) Obstacle: Estimate accurate states of a segmented decentralized system Identify locations of faults while maintaining system stability 11/05/2009NASA Grant URC NCC NNX08BA44A25

26 Motivation Previous Methods resulted in slight discrepancies 11/05/2009NASA Grant URC NCC NNX08BA44A26

27 System Plant Overall System Realization 11/05/2009NASA Grant URC NCC NNX08BA44A27

28 State Observer Currently used for FDI Estimates system states Model of system provides state information Estimates states using input and output 11/05/2009NASA Grant URC NCC NNX08BA44A28

29 State Observer Residual Error Dynamic Error Equation PD Gains State Feedback (L) Observer Design 11/05/200929NASA Grant URC NCC NNX08BA44A

30 State Observer 11/05/2009NASA Grant URC NCC NNX08BA44A30 Simulink Observer Realization

31 State Observer 11/05/2009NASA Grant URC NCC NNX08BA44A31 Simulink Simulation Results Real System Output Observer System Estimates Residuals

32 State Observer 11/05/200932NASA Grant URC NCC NNX08BA44A Residual Error Observer Simulated Output Real System Output Initatied Actuator Fault Observer Discrepencies

33 Outline Problem & Motivation State Observer Kalman Filter Future goals 11/05/2009NASA Grant URC NCC NNX08BA44A33

34 Kalman Filter Methodology –Two Phases: –Predictions Previous Estimate  Current Estimate –Update Current Measurement  Refines Current State estimate Update 11/05/2009NASA Grant URC NCC NNX08BA44A34 [1] http://www.nps.gov/gis/gps/glossary.htm –“A numerical method used to track a time-varying signal in the presence of noise.” [1] –A method of estimating the internal states of a system

35 Kalman Equations 11/05/2009NASA Grant URC NCC NNX08BA44A35 System State Equations Noise Distributions Noise Variances A Priori Equations A Posteriori Equations Kalman Gain Equation

36 Kalman Filter Realization Implementation Scenario 11/05/2009NASA Grant URC NCC NNX08BA44A36

37 Previous Goals Debug Simulink System Model and Kalman Filter Design Noise Model Simulate Subsystem Kalman Filter Import Algorithm into C Test Kalman Design on Testbed 11/05/200937NASA Grant URC NCC NNX08BA44A

38 Simulink: Kalman Filter 11/05/200938NASA Grant URC NCC NNX08BA44A

39 System Model with Additive Noise 11/05/200939NASA Grant URC NCC NNX08BA44A

40 Kalman Filter 11/05/200940NASA Grant URC NCC NNX08BA44A

41 Kalman Gain 11/05/200941NASA Grant URC NCC NNX08BA44A

42 Preliminary Results 11/05/200942NASA Grant URC NCC NNX08BA44A System Simulation Output w/ Additive Noise & Actuated Faults Kalman Estimate

43 Residual Errors 11/05/200943NASA Grant URC NCC NNX08BA44A Kalman Residuals (System Simulation O/P) – (Kalman Estimate O/P) = Actuated Fault Discrepency Step Input

44 Noise Modeling The data collected to model noise is for a single panel i.e. one subsystem Data was collected using LabView to obtain the noise column vector The noise column vector is then used to develop the measurement noise covariance 11/05/200944NASA Grant URC NCC NNX08BA44A

45 Noise Modeling 11/05/200945NASA Grant URC NCC NNX08BA44A

46 Noise Modeling 11/05/200946NASA Grant URC NCC NNX08BA44A

47 Simulation on a subsystem Desired results were not obtained The states, kalman gain, and error covariance have an undesired large magnitude 11/05/200947NASA Grant URC NCC NNX08BA44A

48 Simulation on a subsystem 11/05/200948NASA Grant URC NCC NNX08BA44A

49 Simulation on a subsystem Debugging the problem –Implementing the algorithm on a simpler system that is easier to understand 11/05/200949NASA Grant URC NCC NNX08BA44A

50 Tracking the states of a simpler system 11/05/200950NASA Grant URC NCC NNX08BA44A

51 Tracking the states of a simpler system 11/05/200951NASA Grant URC NCC NNX08BA44A

52 Tracking the states of a simpler system 11/05/200952NASA Grant URC NCC NNX08BA44A

53 Future Tasks Debug Simulink Kalman filter Simulate a controller prior to applying a Kalman filter to a subsystem Replicate to the other 5 subsytems Import algorithm into C Test Kalman design on testbed 11/05/200953NASA Grant URC NCC NNX08BA44A

54 Questions? Thank You 11/05/200954NASA Grant URC NCC NNX08BA44A

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