1. Copyright © Vanderbilt University, Technical University of Budapest Fault-Adaptive Control Technology F33615-99-C-3611 Gabor Karsai Gautam Biswas Sherif Abdelwahed, Tivadar Szemethy Sriram Narasimhan, Tal Pasternak, John Ramirez Gabor Peceli Gyula Simon, Tamas Kovacshazy Feng Zhao Xenofon Koutsoukos, Jim Kurien ISIS, Vanderbilt University Technical University of Budapest, Hungary Xerox PARC http://www.isis.vanderbilt.edu/Projects/Fact/Fact.htm

2. SEC PI Nov 01 Subcontractors & Collaborators TU Budapest Reconfiguration Transient Management Xerox PARC Alternative Hybrid Diagnostics Boeing OCP Controller modeling OCP realization Berkeley Modeling, FDIR Georgia Tech Reconfiguration technology Northrop/Grumman FDIR

3. SEC PI Nov 01 Problem Description, Objective Problem: To maintain control under fault conditions Goal: Technology and tool suite for Fault-Adaptive Control Components: Modeling approach for capturing  Hybrid and discrete models of the plant for both nominal and faulty behavior  Reconfigurable controllers Mode identification and real-time fault-diagnostics  Model-based hybrid and discrete approaches Model-based dynamic selection/synthesis of regulatory controller structures Algorithms for mitigating reconfiguration transients SEC contribution: Integrated Fault detection, isolation, and reconfigurable control

4. SEC PI Nov 01 Open Control Platform Run-time execution environment for hosting: Monitoring and controller software Hybrid and discrete diagnostics modules Controller object library and selector Transient manager component Use OCP as the underlying “OS” Reconfigurable Monitoring and Control System Hybrid Observer Hybrid Diagnostics Failure Propagation Diagnostics Active Model Controller Selector Monitor/ Controller Library Transient Manager Reconfiguration Controller Fault Detector Embedded Models Embedded Models Visual modeling environment for creating: Hybrid bond-graph models Timed failure propagation graph models Controller models (supervisory and regulatory) Technical Approach Summary From models to a run-time system

5. SEC PI Nov 01 Hybrid Modeling New developments Fault detector specifications Variables –FD-> Alarms Modulated components [nonlinearity] Variable –MOD-> (R,C,I,Sf,Se,TF,GY) Controller modeling language SVC + Regulators

6. SEC PI Nov 01 FINITE AUTOMATON Hybrid Observer New developments Tracking autonomous changes Modulated components Observer is composed automatically from component models PLANT CONTROLLER KALMAN FILTER 2 N modes AUTONOMOUS EVENTS CONTROL EVENTS RECALCULATE HYBRID OBSERVER MODELS EST: x k, y k ukuk ykyk N switches MODE CHANGES

7. SEC PI Nov 01 Hybrid Diagnosis Time Line Mode 1 Mode 2Mode 3 Mode 4 Mode 5 Fault Occurs Fault Detected Tracked Trajectory Actual Trajectory T1 T2T3T4 T5 T6 Mode 6 Mode 7 Fault Hypothesis: If controller model is “correct”, fault must have occurred in one of the modes in the mode trajectory New Development: Solution of Hybrid Diagnosis problem for piecewise linear hybrid dynamical systems Presence of fault invalidates tracked mode trajectory Hypothesized fault mode Known Controlled Transition Hypothesized Autonomous Transition Possible current modes Hypothesized intermediate modes Roll Back to find fault hypotheses Roll Forward to confirm fault hypotheses Catch up to current system mode to verify hypotheses against measurements Note: Controller transitions known Autonomous transitions have to be hypothesized

8. SEC PI Nov 01 Hybrid Diagnosis Methodology Tracking, prediction, and analysis of system behavior under fault conditions across discrete mode changes multiplicative Deal with parametric faults (multiplicative) that occur as abrupt changes in parameter values Fault Detection complicated – distinguish between mode change transients and fault transients Sometimes fault detection occurs after mode change occurs  Requires fast roll back process to identify correct model for fault isolation Issue: What to propagate across mode-change boundaries?  To compare against current behavior, fault signatures have to be generated by a quick roll forward process Issue: Autonomous changes cannot be correctly predicted. Tracking process invokes multiple paths Parameter estimation  Fault isolation refinement  Fault magnitude determination Issues Addressed:

9. SEC PI Nov 01 Fault Isolation & Identification From Hybrid Bond Graphs Refined Candidate Set current mode Hypothesis Generation (Back Propagation) Candidate Set Qualitative Hypotheses Refinement Forward Prop + Prog Monitoring Quick Roll Forward Transfer function Models Past Mode Trajectory Temporal Causal Graphs (TCGs) Refined Candidate Set current mode Quantitative Hypotheses Refinement Parameter Estimation Observations Signal to Symbol Generator Mode m i

10. SEC PI Nov 01 Tank2 C2 R3 R6 Tank1 C1 Tank3 C3 R4 R2 R1 R5 Sf1 Sf2 -Valve C–Tank Capacity R–Pipe Resistance Sf–Flow Source Hybrid bond graphs relate parameters to system dynamics Hybrid System Example Three Tank System h i = level of fluid in Tank i H i = height of connecting pipe

11. SEC PI Nov 01 Roll Back Process Qualitative Hypotheses Generation Back propagate through TCG in current mode to identify candidates Back propagate across mode transitions using transition conditions (need to account for reset conditions, and change in plant configuration – invert qualitatively) Repeat same process for previous modes to identify more candidates Fault: Leak in Drain Pipe - Tank 1 Pressure - Tank 2 Pressure - Tank 3 Pressure Transition Fault Occurred Fault Detected System Autonomous Transition Current Mode Candidates = C2+(0-+,-+-,000 ), C1+(-+-,0-+,000 ), R1- (0-+,00-,000 ), R12- (0-+,0+-,000 ) Previous Mode Candidates = C1+(-+-,000,000 ), R1- (0-+,000,000 ) Example 1: Leak in pipe

12. SEC PI Nov 01 Quick Roll Forward Goal: Get to current mode, so parameter estimation can be applied to refine faults and identify fault magnitude Lemma: Sequence of k mode transitions in any order drives the system to the same final model progressive monitoring Requires tracking of transients by progressive monitoring in continuous regions of space. Taylor series expansion defines qualitative fault signatures. Residual r(t) after fault can be described as: Progressive Monitoring: Match qualitative magnitude and slope of measurement signal transient against fault signature Fault signature: qualitative form of derivatives: Qualitative form of

13. SEC PI Nov 01 Quick Roll Forward identify the current mode (roll forward)In continuous case, mismatch implies fault hypothesis is not consistent. However, in hybrid tracking, it may imply that we are not in the right mode. We need to identify the current mode (roll forward) All controlled transitions are known, but we have to hypothesize autonomous transitions since observer can no longer predict them correctly Use fault signatures to hypothesize mode transitions - Tank 1 Pressure - Tank 2 Pressure - Tank 3 Pressure Transition Fault Occurred Fault Detected System Autonomous Transition Current Mode Candidates = C1-(+-+,000,000 ), R1+ (0+-,000,000 ) Signatures don’t match, therefore roll forward by hypothesizing mode transitions Fault: Partial block in pipe Example 2: Block in Pipe Progressive Monitoring with Mode Changes

14. SEC PI Nov 01 Parameter Estimation (Real Time) Derive transfer function model in current mode derived from TCG (signal flow graph) using Mason’s gain rule. ( Computational Complexity: Linear in number of loops ) Parameterized (symbolic) Transfer Function Model of Three Tank System

15. SEC PI Nov 01 Parameter Estimation (Real Time) Initiate fault observer filter for each fault hypothesis substitute nominal values for all but the faulty parameter Initiate least squares estimator for parameter estimation compute parameter values from g and h estimates. Check consistency Test for convergence as more measurements obtained identifies true fault candidate consistency implies predicted parameter value substituted into model again tracks system accurately

16. SEC PI Nov 01 Discrete Diagnostics Algorithm New developments Correct diagnosis of graphs with loops Diagnostics with ranked hypotheses Started: Discrete diagnostics for hybrid systems The FPG structure is dependent on the mode RefineHypothesis( set Alarms) { static set NewFailureModes, NewMissingUpstream, MissingAncestors, PromotedNewFailureModes; const static map Descendant, Ancestor; NewFailureModes = RelationalProduct(Descendant,Alarms) && (-Hypotheses); Hypotheses |= NewFailureModes; // Add NewFailureModes to hypothesis set MissingAncestors = (RelationalProduct(Alarms,Ancestor) && (-MissingUpstream) && (-AlreadyRinging)); NewMissingUpstream = RelationalProduct(Hypotheses,Descendant) && MissingAncestors; MissingUpstream |= NewMissingUpstream; AlreadyRinging |= Alarms; // Increment rank of faults which have new supporting alarms and no new missing upstream alarms PromotedNewFailureModes = RelationalProduct(Descendant,Alarms) && (-RelationalProduct(Descendant,NewMissingUpstream)); }

17. SEC PI Nov 01 Descendants: FModes X Alarms Alarms X& - Hypo U Hypo’ Ancestors: Alarms X Alarms X Missing Upstream Already Ringing & -- & Missing Upstream’ U Already Ringing’ U X & Promoted FModes Promoted FModes Discrete Diagnostics Algorithm Algorithm flow

18. SEC PI Nov 01 Combine the results of multiple (2) diagnostic reasoners Maps the specific hypotheses into Bond Graph elements Intersecting subsets (  Listed by ANY (  Listed by EACH (  TopRank by ANY (  TopRank by EACH Agreement : when |  |  Fusion algorithm Integrating the hybrid and discrete diagnostics All dynamic data (incl. diagnostics results) is available via the Active State Model

19. SEC PI Nov 01 Controller Reconfiguration Model Problem Setting The System A hybrid system H with: Linear cont. dynamics: f q = A q x+B q u Piecewise-linear (PL) discrete constraints: Inv q, Init q, G q,q’ are PL The specification the system has to remain in a given safe region defined by a set of PL constraints. Piecewise Linear Hybrid System Configuration engine Diagnoser Observer detects faulty components provides the current value of the system parameters provides enough information to observe the current state Controller compute the current system state adjust the controller for the new system parameters assumes finite control policies provide stable and efficient transitions between controllers components measurements of variables, states parameters update control input Sensors Alarms Samplers Switches Valves Regulators

20. SEC PI Nov 01 Current systems data Hybrid System Controller Synthesis Discrete Abstraction Divide the state space into finite set of regions In any region, the system can be driven to the adjacent regions Supervisory Control based on the abstract state machine obtained by the partition it is required to move the system from current region to safe region movement is based on the discrete supervisor Continuous Control continuous controller is established for each region drive the system from a region to the guard (surface) of the next one. Hybrid model parameters current discrete state current continuous state Global discrete observer Local continuous observer discrete input continuous input global abstract control local detailed control Discrete and continuous diagnoser Controller Reconfiguration Approach

21. SEC PI Nov 01 Current Focus Controller: Parameter Design Procedures Resource Requirements - run-time cost - design proc cost - reconfiguration cost Performance metrics Settling time, overshoot Reconfiguration Support Initial state Injection sequence S: signal flow graph P: parameter set x: state variables Services are used: - off-line (design-time) by system designer - on-line (run-time) by designer/constructor algorithms Transient management Reconfigurable controller description

22. SEC PI Nov 01 Current Focus The Supervisory Controller supports the following Controller specification techniques: Set given Design S given, P calculated based on control objective Construct Select from given {S i } based on control objective [Initial values for x are calculated by the Transient Management Algorithm] Transient management Controller specification in SVC

23. SEC PI Nov 01 [Controller Services] Current Focus Construct decision making: Constraint satisfaction (optimization) based on Performance requirements Resource requirements Performance specifications [Supervisory Controller] Available resources [Current System State] Resource requirementsPerformance metrics Transient management Controller description hierarchy Abstract controller (root) Controller variants Physical realizations (HW/SW)

24. SEC PI Nov 01 Real-life example: Aircraft Fuel System Obtained engineering documents and simulation data from Boeing Built Hybrid Bond Graph model of the system Started testing the HOBS and DIAG components using simulated data

25. SEC PI Nov 01 Schematic of Fuel Transfer Systems and GME model JoinFour LWTP Component-Based Hierarchical GME Model Fuel Transfer Schematic Symmetric Transfer and Wing tanks Two Feed Tanks that supply fuel to engine Controller maintains fuel supply and CG of aircraft Behavior: Complex Hybrid Dynamics Multiple pumps and pathways to accommodate pump failure and leaks

26. SEC PI Nov 01 Fuel Transfer Schematic and Bond Graph Hybrid Bond Graph Model of System Hybrid Bond Graph: Topological Model of energy + signal model f system Captures hybrid state space + temporal causal model of system dynamics Faults parameterized in representation (pump failures + pipe and tank leaks + valve failures) Used for hybrid observer + fault detection, isolation, and identification Enables tracking of system behavior in nominal plus faulty modes of operation

27. SEC PI Nov 01 Project Tasks/Schedule/Status 20002001 20022003 Lab prototype Prototype HOBs, TCG FPG diag, Transient mgmt tech Embeddable version Controller Modeling Reconfig mgr Embedded version Plant Modeling Framework 1 st OCP Integration Analysis technology Analysis tools: Diagnosability (FPG) Feasibility (HYB) Consistency/completeness (RC)

28. SEC PI Nov 01 Next Milestones Next 6 months Implement CML run-time support Hierarchical FSM for supervisory control Regulator blocks (OCP components) Finish improved discrete diagnostics Implement prototype controller selector Trials on the A/C fuel system Integrate on OCP Integrate with Xerox

29. SEC PI Nov 01 Technology Transition/Transfer Boeing IVHM Group Aircraft Fuel System models (DEMO) Testing fault diagnostics using simulated data (provided by Boeing) Plan: Develop a full FACT application using the fuel system as example GE Aircraft Engines First contact with their Advanced Controls group Potential collaborations NASA Intelligent System Group Recently started project Application area: advanced life-support system

30. SEC PI Nov 01 Program Issues PARC integration work OCP: Specific challenge problem(s) Precise documentation Transfer to other DoD programs

31. SEC PI Nov 01 Pump GO BACK

32. SEC PI Nov 01 LWTP PumpPipe1 Tank GO BACK

33. SEC PI Nov 01 Tank GO BACK

34. SEC PI Nov 01 Pipe GO BACK

35. SEC PI Nov 01 JoinFour GO BACK