Скачать презентацию Scenario Description Maggie Stringfellow Herring October 23 2003 Скачать презентацию Scenario Description Maggie Stringfellow Herring October 23 2003

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Scenario Description Maggie Stringfellow Herring October 23, 2003 R&TD DSAN M&C Scenario Description Maggie Stringfellow Herring October 23, 2003 R&TD DSAN M&C

Antenna Tracking Scenario Summarized from an Oct. 11 e-mail from Scott Morgan: • Group Antenna Tracking Scenario Summarized from an Oct. 11 e-mail from Scott Morgan: • Group of 20 antennas tracking a target • Working fine for 4 hours • One antenna fails to maintain pointing • M&C must drop that antenna from signal combining • M&C allocates a spare antenna and brings it into the group Feb. 24, 2005 R&TD DSAN M&C 2

Sample Operational Scenario We are using this operational scenario from Scott Morgan to focus Sample Operational Scenario We are using this operational scenario from Scott Morgan to focus our initial analysis and design efforts. Initial conditions: • 20 antennas (#1 -#20) involved in a spacecraft tracking pass • one target in the beam (not a multiple spacecraft support) • 5 hours remain in the activity • the activity has been going for 4 hours • 15 antennas are required to provide the requested snr • 3 antennas (#11, #12, #13) are currently being calibrated (they are not pointing at the spacecraft) • X-band, RCP downlink • no uplink Scenario: • A. antenna controller #5 reports a high current load in the azimuth motor [T 0] • B. antenna controller #5 reports that pointing error exceeds the maximum tolerance [T 0+. 5 sec] • C. signal processing correlator reports that it is unable to correlate signal from antenna #5 [T 0+. 6 sec] • D. signal processing correlator removes signal #5 from the correlation (weight=0) [T 0+. 7 sec] • E. M&C directs signal processing to remove signal from antenna #5 from the correlation [T 0+1 sec] • F. M&C issues a "shutdown" command to antenna #5 [T 0+ 1 sec] • G. M&C aborts the calibration activity for antenna #11, #12, #13 (has multiple steps) [T 0+1 sec] • H. M&C directs antenna #11 to point at the spacecraft target [T 0+1. 2 sec] • I. antenna controller #11 reports that pointing errors are within tolerance [T 0+6. 2 sec] • J. M&C directs signal processing to add X-band, RCP signal from antenna #11 into the correlation [T 0+6. 4 sec] • K. signal processing correlator reports acceptable correlation using signal from antenna #11 [T 0+7. 4 sec] • L. M&C removes antenna #5 from the available resource pool (picked up by scheduling) [T 0+10 sec] • M. M&C issues a service request for antenna #5 [T 0+12 sec] Feb. 24, 2005 R&TD DSAN M&C 3

Receive Array & Scenario Timeline A B C D E F G H I Receive Array & Scenario Timeline A B C D E F G H I J K L M timeline RF from SRC Analog Ant 11 Ant Mech Focused A-1 Analog Power Ant 5 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 5 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 5 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 4

Receive Array & Scenario Timeline A. antenna controller #5 reports a high current load Receive Array & Scenario Timeline A. antenna controller #5 reports a high current load in the azimuth motor [T 0] A B C D E F G H I J K L M timeline RF from SRC Analog Ant 11 Ant Mech Focused A-1 Analog Power Ant 5 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 5 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 5 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 5

Receive Array & Scenario Timeline B. antenna controller #5 reports that pointing error exceeds Receive Array & Scenario Timeline B. antenna controller #5 reports that pointing error exceeds the maximum tolerance [T 0+. 5 sec] A B C D E F G H I J K L M timeline RF from SRC Analog Ant 11 Ant Mech Focused A-1 Analog Power Ant 5 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 5 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 5 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 6

Receive Array & Scenario Timeline C. signal processing correlator reports that it is unable Receive Array & Scenario Timeline C. signal processing correlator reports that it is unable to correlate signal from antenna #5 [T 0+. 6 sec] A B C D E F G H I J K L M timeline RF from SRC Analog Ant 11 Ant Mech Focused A-1 Analog Power Ant 5 Mech Elec Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 5 Elec SP A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 5 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 7

Receive Array & Scenario Timeline D. signal processing correlator removes signal #5 from the Receive Array & Scenario Timeline D. signal processing correlator removes signal #5 from the correlation (weight=0) [T 0+. 7 sec] A B C D E F G H I J K L M timeline RF from SRC Analog Ant 11 Ant Mech Focused A-1 Analog Power Ant 5 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 5 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 5 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 8

Receive Array & Scenario Timeline E. M&C directs signal processing to remove signal from Receive Array & Scenario Timeline E. M&C directs signal processing to remove signal from antenna #5 from the correlation [T 0+1 sec] A B C D E F G H I J K L M timeline Signal Inclusion Tracking Target {1 -4, 6 -10, 14 -20} {1 -10, 14 -20} RF from SRC Analog Ant 11 Ant Mech Focused A-1 Analog Power Ant 5 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 5 Elec A-1 IF Analog Arg Support Facility Tracking Calibration Target { 11 -13 } Ant 11 Ant SP SP Ant 5 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 9

Receive Array & Scenario Timeline F. M&C issues a Receive Array & Scenario Timeline F. M&C issues a "shutdown" command to antenna #5 [T 0+ 1 sec] A B C D E F G H I J K L M timeline Offline & ready Offline & not ready Signal Inclusion Tracking Target {1 -4, 6 -10, 14 -20} Tracking On-pnt Shutdown Off-pnt RF from SRC Analog (5) (1 -20) (1 -4, 6 -20) Focused A-1 Ant 11 Ant Mech Analog Antenna Control Building Feb. 24, 2005 Stowing Idle Ant 11 Ant Elec Power Ant 5 Mech Tracking Calibration Target { 11 -13 } Ant 5 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 5 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 10

Receive Array & Scenario Timeline G. M&C aborts the calibration activity for antenna #11, Receive Array & Scenario Timeline G. M&C aborts the calibration activity for antenna #11, #12, #13 (has multiple steps) [T 0+1 sec] A B C D E F G H I J K L M timeline (1 -4, 6 -10, (1 -4, 6 -20) 14 -20) Offline & ready Offline & not ready Signal Inclusion Tracking Target {1 -4, 6 -10, 14 -20} Tracking On-pnt Shutdown Off-pnt (11 -13) RF from SRC Analog Ant 11 Ant Mech (5) Focused A-1 Analog (11 -13) Antenna Control Building Feb. 24, 2005 Stowing Idle Ant 11 Ant Elec Power Ant 11 Mech Tracking Calibration Target { } 11 -13 } Ant 11 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 11 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 11

Receive Array & Scenario Timeline H. M&C directs antenna #11 to point at the Receive Array & Scenario Timeline H. M&C directs antenna #11 to point at the spacecraft target [T 0+1. 2 sec] A B C D E F G H I J K L M timeline Offline & ready Offline & not ready Signal Inclusion Tracking Target {1 -4, 6 -10, 14 -20} Tracking On-pnt Shutdown Off-pnt (11) RF from SRC Analog Ant 11 Ant Mech Stowing Focused A-1 Analog Idle Ant 11 Ant Elec (11) Elec Power Ant 11 Mech Antenna Control Building Feb. 24, 2005 Tracking Calibration Target { } Ant 11 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 11 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 12

Receive Array & Scenario Timeline I. antenna controller #11 reports that pointing errors are Receive Array & Scenario Timeline I. antenna controller #11 reports that pointing errors are within tolerance [T 0+6. 2 sec] A B C D E F G H I J K L M timeline (11) Offline & ready Offline & not ready Signal Inclusion Tracking Target {1 -4, 6 -10, 14 -20} Tracking On-pnt Shutdown Off-pnt (11) RF from SRC Analog Ant 11 Ant Mech Stowing Focused A-1 Analog Idle Ant 11 Ant Elec Power Ant 11 Mech Antenna Control Building Feb. 24, 2005 Tracking Calibration Target { } Ant 11 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 11 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 13

Receive Array & Scenario Timeline J. M&C directs signal processing to add X-band, RCP Receive Array & Scenario Timeline J. M&C directs signal processing to add X-band, RCP signal from antenna #11 into the correlation [T 0+6. 4 sec] A B C D E F G H I J K L M timeline Signal Inclusion Tracking Target {1 -4, 6 -10, 14 -20} 6 -11, RF from SRC Analog Ant 11 Ant Mech Focused A-1 Analog Power Ant 11 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 11 Elec A-1 IF Analog Arg Support Facility Tracking Calibration Target { } Ant 11 Ant SP SP Ant 11 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 14

Receive Array & Scenario Timeline K. signal processing correlator reports acceptable correlation using signal Receive Array & Scenario Timeline K. signal processing correlator reports acceptable correlation using signal from antenna #11 [T 0+7. 4 sec]. A B C D E F G H I J K L M timeline RF from SRC Analog Ant 11 Ant Mech Focused A-1 Analog Power Ant 11 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 11 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 11 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 15

Receive Array & Scenario Timeline L. M&C removes antenna #5 from the available resource Receive Array & Scenario Timeline L. M&C removes antenna #5 from the available resource pool (picked up by scheduling) [T 0+10 sec] A B C D E F G H I J K L M timeline (5) Offline & ready Offline & not ready Tracking On-pnt Shutdown Off-pnt Stowing (5) RF from SRC Analog Ant 1 Mech Focused A-1 Analog Ant 1 Elec Power Ant 5 Mech Antenna Control Building Feb. 24, 2005 Idle Ant 5 Elec A-1 IF Analog Arg Support Facility Ant 1 SP SP Ant 5 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 16

Receive Array & Scenario Timeline M. M&C issues a service request for antenna #5 Receive Array & Scenario Timeline M. M&C issues a service request for antenna #5 [T 0+12 sec] A B C D E F G H I J K L M timeline RF from SRC Analog Service Request Ant 11 Ant Mech Issued Mech Focused A-1 Analog Power Ant 5 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 5 Elec A-1 IF Analog Arg Support Facility Ant 11 Ant SP SP Ant 5 SP Phased, conditioned, digitized A-1 IF W W Correlations Correlator & Combiner Combined Signal ITI TR&C Array Signal Processing Cluster Control Building R&TD DSAN M&C 17

Analysis of System Under Control October 23, 2003 R&TD DSAN M&C Analysis of System Under Control October 23, 2003 R&TD DSAN M&C

General Approach DSAN operations scenario 1 You are here guides 2 Analysis of system General Approach DSAN operations scenario 1 You are here guides 2 Analysis of system under control produces 3 Physics model of system under control informs 4 Feb. 24, 2005 informs M&C software design Goal/macro-based DSAN operations R&TD DSAN M&C 5 19

Complete State Effects Diagram Physics Model Screendump from database tool. Feb. 24, 2005 R&TD Complete State Effects Diagram Physics Model Screendump from database tool. Feb. 24, 2005 R&TD DSAN M&C 20

Notation of State Effects Diagram Notation Ant N Elect Power Msmt: Ant N Elect Notation of State Effects Diagram Notation Ant N Elect Power Msmt: Ant N Elect Power Cmd: Signal Inclusion A Feb. 24, 2005 B Meaning A physical state variable of the system under control, identified because of its relevance to “how things work”. A measurement from the system under control. It provides evidence about the values of state variable(s) that affect it. A command that affects the system under control. A command affects the values of one or more state variables. ‘A’ affects ‘B’, based on physics and design. A state variable can affect other state variables and measurements. A command can affect one or more state variables. R&TD DSAN M&C 21

Assumptions • Approach – Scenario Driven • Models inferred from DSAN documents – Incremental; Assumptions • Approach – Scenario Driven • Models inferred from DSAN documents – Incremental; Small Deltas in Scope – Qualitative Models • One Subarray • Treat each subsystem as a black box. – Antenna Mechanical Subsystem model based on modes of the antenna rather than, e. g. , az/el pointing – Omitted measurements that provide redundant information • Correlator Signal Weights Feb. 24, 2005 R&TD DSAN M&C 22

Simplification • Simplification of the Signal Flow Path – Omitted Antenna Signal Processing – Simplification • Simplification of the Signal Flow Path – Omitted Antenna Signal Processing – Omitted Antenna Electronics – Omitted Signal Delays • States not mentioned in the scenario were eliminated. – Health & Power – Array Support Facility – Ant. Electronics, Ant. Signal Processing • Combined state variables – Correlator & Combiner states – Analog Signal & Analog IF & Digital IF – Background Signal & Noise states Feb. 24, 2005 R&TD DSAN M&C 23

Signal Flow Diagram & Physics Model Target & Background State RF from SRC Analog Signal Flow Diagram & Physics Model Target & Background State RF from SRC Analog Ant_N Mech & Op. Mode State A-1 Focused Ant 11 Ant Mech Analog Power Ant 11 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 11 Elec A-1 IF Analog Arg Support Facility Ant_N Received Signal Phased, State Ant 11 Ant SP SP conditioned, digitized A-1 IF Correlator & Combiner State Correlator W & Combiner W Array Signal Processing Ant 11 SP Cluster Control Building R&TD DSAN M&C Msmt: Correlation Matrix Correlations Combined Signal State ITI TR&C 24

Scoped out Physics Model Feb. 24, 2005 R&TD DSAN M&C 25 Scoped out Physics Model Feb. 24, 2005 R&TD DSAN M&C 25

Complete State Effects Diagram Physics Model Screen dump from database tool. Feb. 24, 2005 Complete State Effects Diagram Physics Model Screen dump from database tool. Feb. 24, 2005 R&TD DSAN M&C 26

Signal Flow Diagram & Physics Model Target & Background State RF from SRC Analog Signal Flow Diagram & Physics Model Target & Background State RF from SRC Analog Ant_N Mech & Op. Mode State A-1 Focused Ant 11 Ant Mech Analog Power Ant 11 Mech Antenna Control Building Feb. 24, 2005 Ant 11 Ant Elec Ant 11 Elec A-1 IF Analog Arg Support Facility Ant_N Received Signal Phased, State Ant 11 Ant SP SP conditioned, digitized A-1 IF Correlator & Combiner State Correlator W & Combiner W Array Signal Processing Ant 11 SP Cluster Control Building R&TD DSAN M&C Msmt: Correlation Matrix Correlations Combined Signal State ITI TR&C 27

Feb. 24, 2005 R&TD DSAN M&C 28 Feb. 24, 2005 R&TD DSAN M&C 28

Antenna_N Received Signal Model If Ant_N Mechanical Pointing, Power, Op. Mode & Health is Antenna_N Received Signal Model If Ant_N Mechanical Pointing, Power, Op. Mode & Health is Tracking & Healthy, and Target Signal State is Present then Received Signal has a Target Content Factor (TCF) value of One. else Received Signal has a TCF value of Zero. Feb. 24, 2005 R&TD DSAN M&C 29

Feb. 24, 2005 R&TD DSAN M&C 30 Feb. 24, 2005 R&TD DSAN M&C 30

Antenna_N Mechanical Pointing, Power, Op. Mode and Heath Offline & not ready Tracking On-pnt Antenna_N Mechanical Pointing, Power, Op. Mode and Heath Offline & not ready Tracking On-pnt Off-pnt msmt On-pnt msmst Repaired msmt Offline & ready Go offline cmd Power off Come online cmd Shutdown Power off Off-pnt Begin Tracking Power on Idle End-of-profile or go-idle Motor current goes high Unhealthy Motor flagged as “repaired” For now, only one fault mode Healthy Feb. 24, 2005 R&TD DSAN M&C 31

Physics Models, 2 • Ant_N Received Signal If Ant_N Mech Op. Mode & Health Physics Models, 2 • Ant_N Received Signal If Ant_N Mech Op. Mode & Health = not shutdown or offline if Target Signal State = present and Ant_N Mech Op. Mode & Health = on-point then: Target + Noise + Background else: Noise + Background • Background & Noise Signal State Always present • Target Signal State Present or Not Present Feb. 24, 2005 R&TD DSAN M&C 32

Physics Models, 3 • Array SP Correlator & Combiner State Signal Inclusion: A list Physics Models, 3 • Array SP Correlator & Combiner State Signal Inclusion: A list of all the contributing signals to a subarray. These signals include signals that are weighted low and signals from calibrating antennas. • Signal Inclusion Command Model – Command models represent how states are affected by commands A Signal Inclusion Command can add or remove signals to the correlator & combiner. example: before cmd Cmd: Signal Inclusion: Remove signal 5 after Feb. 24, 2005 Signal Inclusion of antennas tracking target: { 1 - 10 } Signal Inclusion of antennas tracking calibration target: { 11 - 13 } Target Signal Inclusion = { 1 – 4, 6 – 10 } Calibration Signal Inclusion = { 11 -13 } R&TD DSAN M&C 33

Physics Models, 4 • Combined Signal State After the individual signals have been weighted, Physics Models, 4 • Combined Signal State After the individual signals have been weighted, the Combined Signal State is their scaled content factors. (“Content factor” is the number of target signals and background signals & noise signals received from an antenna. ) example: Ant 1 receives Target + Background & Noise Ant 2 receives Target + Background & Noise Ant 3 receives Background & Noise Assuming all signals are weighted 1 Target “content factor” is 2 and background & noise “content factor” is 3. Assuming signal 3 is weighted 0 Target “content factor” is 2 and background & noise “content factor” is 2. Feb. 24, 2005 R&TD DSAN M&C 34

Measurement Model • Measurement Models – Represent how measurements are affected by states Antennas Measurement Model • Measurement Models – Represent how measurements are affected by states Antennas 2 3 1 A A B 2 A A B 3 • Correlation Matrix Measurement 1 B B A Correlation matrix measurement equals Correlations (signal from antenna 1, signal from antenna 2, . . . , signal from antenna N ) where each correlation ij in the matrix will be above correlation threshold or below threshold. Each ij in the matrix is above threshold if its target content factor is greater than or equal to their noise content factor. Otherwise ij is below threshold. A represents above threshold and B represents below threshold. The signals from antennas one and two are Target + Background & Noise and the signal from antenna three is just Background & Noise. Feb. 24, 2005 R&TD DSAN M&C 35

Analysis of System Under Control: Summary • Models captures in the State Database tool Analysis of System Under Control: Summary • Models captures in the State Database tool – Central repository for both Systems and Software engineers • Models directly used in simulations • Models can be easily changed from low fidelity to high fidelity. – We have already worked up higher fidelity models for increment 2 • Models are used to inform the design of the control system. – Systems & Software Design Unification Feb. 24, 2005 R&TD DSAN M&C 36

Establishing a Control Point of View Feb. 24, 2005 R&TD DSAN M&C 37 Establishing a Control Point of View Feb. 24, 2005 R&TD DSAN M&C 37

Where to Begin • Control is about changing things to meet your objectives – Where to Begin • Control is about changing things to meet your objectives – There is an intrinsic notion of one thing being responsible for another – To think in terms of control, it is important to separate… …what is doing the controlling from what is being controlled • We do not assume a priori that interactions adhere to any particular hierarchy • Therefore, we adopt the notion of a system of cooperating controllers — a Control System • What is the Control System? Feb. 24, 2005 R&TD DSAN M&C 38

Decomposition for Control The Central Role of Models Control S/W The Control System has Decomposition for Control The Central Role of Models Control S/W The Control System has cognizance over the System Under Control System measurements Environment Hardware Other S/W commands Feb. 24, 2005 System Under Control Model of the… System Under Control · Control System · The functionality of control is separate from the rest of the system · A model of the system under control can be used to inform the design and operation of the control system · This avoids self-reference, which simplifies the description of control functions · System Under Control · The vehicle and its environment are considered together as an integrated entity · Certain key software elements, such as hardware I/O and data management and transport functions, are included in the system under control · This partitioning presents an abstract interface that can be tailored to be modeled more easily than arbitrary functional interfaces · It may have control functions embedded within it (usually localized and comparatively simple), but these are just more behavior to be modeled R&TD DSAN M&C 39

The Fundamental Message Model of the… System Under Control System measurements commands System Under The Fundamental Message Model of the… System Under Control System measurements commands System Under Control Feb. 24, 2005 • To understand the control system … what it needs to do, what it needs to be… you need to carefully delineate it from the system under control and exploit your understanding of it in terms of models of the system under control R&TD DSAN M&C 40

Facts of Life • Somehow, the models systems engineers understand must inform what software Facts of Life • Somehow, the models systems engineers understand must inform what software designers build – Whether overt and explicit, or hidden quietly in the minds of the engineers, models have always existed – Understanding and modeling are essentially the same thing – Software design is ultimately a reflection of this understanding, and therefore a reflection of these models • To the extent the software design reflects the systems engineer’s understanding, the software will perform as the systems engineers desire That is, … Feb. 24, 2005 R&TD DSAN M&C 41

System Software is a Surrogate for Systems Engineers… and Software Engineers perform the transformation System Software is a Surrogate for Systems Engineers… and Software Engineers perform the transformation Feb. 24, 2005 R&TD DSAN M&C 42

This Is Where State Analysis Steps In State analysis asserts these basic principles: Control, This Is Where State Analysis Steps In State analysis asserts these basic principles: Control, which subsumes all aspects of system operation, can be understood and exercised only through models Models ought to be explicitly identified and used in a way that assures consensus among systems engineers The manner in which models inform software design and operation ought to be direct, requiring minimal translation Feb. 24, 2005 R&TD DSAN M&C 43

Simple Example: A Camera on a Scan Platform • The camera turns on the Simple Example: A Camera on a Scan Platform • The camera turns on the gimbaled platform to point at a target • Picture data from the camera is stored separately • A heater can keep it warm when the camera is OFF · Since control is about · change, we need a way to talk about change This is accomplished with the notion of State Feb. 24, 2005 Example System Heater Camera Data Platform R&TD DSAN M&C 44

Modeling the System Under Control • Six State Variables are defined – Camera Temperature: Modeling the System Under Control • Six State Variables are defined – Camera Temperature: real number in °C – Camera Heater: ON or OFF and so on • They are related as shown in a State Effects Diagram diagram • Models describe these effects in detail: – A thermal model describes temperature versus camera and heater power – The camera powers ON in idle mode; it can’t take pictures when OFF – What’s in a picture depends on where the camera is pointed and how the camera is operated Feb. 24, 2005 and so on… State Effects Diagram Camera Temp’ A B Camera Data Status Camera Mode Camera Heater Camera Power R&TD DSAN M&C State VARIABLE A affects state variable B Platform Pointing 45

A Simple Sequence Taking a Picture Operator’s View: 1: 00 PM + 2 m A Simple Sequence Taking a Picture Operator’s View: 1: 00 PM + 2 m + 8 m Camera Heater OFF Camera ON Turn platform to target Turn done The sequence used to take a picture of some target might look something like this Take picture + 1 m + 2 m • Camera OFF Camera Heater ON State Effects Diagram · This sequence affects the Camera Temp’ states we have defined Camera Power R&TD DSAN M&C A B Camera Data Status Camera Mode Camera Heater Feb. 24, 2005 State VARIABLE A affects state variable B Platform Pointing 46

Modeling the System Under Control • Identifies the important state variables in the system Modeling the System Under Control • Identifies the important state variables in the system • Describes the causal effects among the state variables, commands and measurements (under both nominal and offnominal situations) • Uses any appropriate representation, e. g. , differential equations, tables, state charts, pseudo-code, plain text, etc. • Behavioral models of this type are invaluable, in that they can be used for multiple purposes, including: Feb. 24, 2005 – Informing the design of flight and ground software (e. g. , estimation and control algorithms); – Using them directly in model-based estimation & control software (e. g. , Kalman filters); – Informing the design of fault protection mechanisms (models of nominal and off-nominal behavior can feed into Fault Tree and FMECA analyses, risk analyses, and fault monitor/response design); – Feeding directly into simulations; and – Using them for planning and scheduling purposes (including automated approaches, either on the ground or onboard the spacecraft). R&TD DSAN M&C 47

Modeling the System Under Control • Iterative process for discovering state variables of the Modeling the System Under Control • Iterative process for discovering state variables of the system under control and for incrementally constructing the model: Feb. 24, 2005 1. Identify needs – define the high-level objectives for controlling the system. 2. Identify state variables that capture what needs to be controlled to meet the objectives, and define their representation. 3. Define state models for the identified state variables – these may uncover additional state variables that affect the identified state variables. 4. Identify measurements needed to estimate the state variables, and define their representation. 5. Define measurement models for the identified measurements – these may uncover additional state variables. 6. Identify commands needed to control the state variables, and define their representation. 7. Define command models for the identified commands – these may uncover additional state variables. 8. Repeat steps 2 -7 on all newly discovered state variables, until all relevant variables and effects are accounted for. 9. Return to step 1 to identify additional objectives, and proceed with R&TD DSAN M&C 48

State Effects Diagrams and Models Feb. 24, 2005 R&TD DSAN M&C 49 State Effects Diagrams and Models Feb. 24, 2005 R&TD DSAN M&C 49

State Effects Diagrams and Models Feb. 24, 2005 R&TD DSAN M&C 50 State Effects Diagrams and Models Feb. 24, 2005 R&TD DSAN M&C 50

State Effects Diagrams and Models Feb. 24, 2005 Asynchronous to the execution cycle of State Effects Diagrams and Models Feb. 24, 2005 Asynchronous to the execution cycle of the antenna pointing, the antenna may be given a new command. If the command is valid and the antenna is healthy, powered and in tracking mode, the new command will become the active command. Specifically: if antenna health == HEALTHY, power == POWERED, opmode == TRACKING and NEW_CMD is valid CMD = NEW_CMD TRACK_SEGMENT = 0 TRAJ_IN_PROGRESS = true else NEW_CMD is ignored endif Every execution cycle, the antenna pointing will exhibit the following behavior: set t = t_next and t_next = t + delta_t if antenna health == HEALTHY, power == POWERED, and opmode == TRACKING if TRAJ_IN_PROGRESS == true set TRACK_SEGMENT_COMPLETE = ((az(t) == CMD[TRACK_SEGMENT]. az) AND (el(t) == CMD[TRACK_SEGMENT]. el)) set TRACK_COMPLETE = (TRACK_SEGMENT == length(CMD) 1) AND TRACK_SEGMENT_COMPLETE if (TRACK_COMPLETE) then hold position (done with track) el_rate = 0 az_rate = 0 el_angle(t_next)=el_angle(t) az_angle(t_next)=az_angle(t) set TRAJ_IN_PROGRESS = false else if TRACK_SEGMENT_COMPLETE then increment TRACK_SEGMENT endif // compute az_rate and el_rate for segment TRACK_SEGMENT el_rate = (CMD[TRACK_SEGMENT]. el - el(t)) / (CMD[TRACK_SEGMENT]. t - t) az_rate = (CMD[TRACK_SEGMENT]. az - az(t)) / (CMD[TRACK_SEGMENT]. t - t) // apply az_rate and el_rate for first delta_t in segment TRACK_SEGMENT R&TD = az(t) M&C az(t_next) DSAN + az_rate * delta_t, same for el 51

Model Informs Software Design IMU Op. Mode Cmd IMU Power, Op. Mode & Health Model Informs Software Design IMU Op. Mode Cmd IMU Power, Op. Mode & Health IMU Power Switch State & Health IMU Power Switch Msmnt IMU Msmnt Feb. 24, 2005 IMU Power Switch Cmd R&TD DSAN M&C 52

State Discovery is About Physical States Control System Elaboration, projection, & scheduling • A State Discovery is About Physical States Control System Elaboration, projection, & scheduling • A state is a property of a thing – Mass of a spacecraft – Pointing of a camera – Etc. State variables Intent • Physical States Knowledge Estimation Execution Control measurements – Exist in System Under Control – Includes hardware states, environment states, and even software states commands System Under Control Feb. 24, 2005 Physical states identified during analysis will later be implemented as state variables in the control system software R&TD DSAN M&C 53

State Discovery • Quiz: Identify physical states relevant to the control problem. camera temperature State Discovery • Quiz: Identify physical states relevant to the control problem. camera temperature actuator health switch position cmd System Under Control meas sensor scale factor Switch Actuator battery voltage sensor health Switch Sensor Temperature Sensor Power Switch Battery Heater Camera sensor bias heat flow heater resistance ambient temperature Feb. 24, 2005 heater health R&TD DSAN M&C 54

State Discovery Start with What You Need to Control • Identify a state variable State Discovery Start with What You Need to Control • Identify a state variable that the control system needs to control Camera Temperature Legend: Feb. 24, 2005 = state variable = measurement = command R&TD DSAN M&C 55

State Discovery Identify Affecting States (1) • Work your way out from objective state State Discovery Identify Affecting States (1) • Work your way out from objective state variables to state variables that affect them Heater Heat Flow Camera Op Mode Legend: Feb. 24, 2005 = state variable = measurement = command Camera Temperature Camera Thermal Mass R&TD DSAN M&C Ambient Temperature Thermal Resistance 56

State Discovery Identify Affecting States (2) • Work your way out to other affecting State Discovery Identify Affecting States (2) • Work your way out to other affecting state variables Switch Position Heater Health & Resistance Battery Voltage Legend: Feb. 24, 2005 = state variable = measurement = command Heater Heat Flow Camera Op Mode Camera Temperature Camera Thermal Mass R&TD DSAN M&C Ambient Temperature Thermal Resistance 57

State Discovery Add Measurements (1) • What measurements provide evidence about states we’ve identified? State Discovery Add Measurements (1) • What measurements provide evidence about states we’ve identified? Switch Pos. Measurement Temperature Measurement Switch Position Heater Health & Resistance Battery Voltage Legend: Feb. 24, 2005 = state variable = measurement = command Heater Heat Flow Camera Op Mode Camera Temperature Camera Thermal Mass R&TD DSAN M&C Ambient Temperature Thermal Resistance 58

State Discovery Add Measurements (2) • How are these measurements affected by other states? State Discovery Add Measurements (2) • How are these measurements affected by other states? Switch Pos. Measurement Temperature Measurement Switch Position Heater Health & Resistance Battery Voltage Legend: Feb. 24, 2005 = state variable = measurement = command Switch Sensor Health Heater Heat Flow Camera Op Mode Camera Temperature Camera Thermal Mass R&TD DSAN M&C Temperature Sensor Health Ambient Temperature Sensor Scale Factor Sensor Bias Thermal Resistance 59

State Discovery Add Commands (1) • What commands do we have to influence the State Discovery Add Commands (1) • What commands do we have to influence the controlled state? Switch Command Switch Pos. Measurement Temperature Measurement Switch Position Heater Health & Resistance Battery Voltage Legend: Feb. 24, 2005 = state variable = measurement = command Switch Sensor Health Heater Heat Flow Camera Op Mode Camera Temperature Camera Thermal Mass R&TD DSAN M&C Temperature Sensor Health Ambient Temperature Sensor Scale Factor Sensor Bias Thermal Resistance 60

State Discovery Add Commands (2) • What other states contribute to the effect of State Discovery Add Commands (2) • What other states contribute to the effect of the command? Switch Command Switch Pos. Measurement Temperature Measurement Switch Actuator Health Switch Position Heater Health & Resistance Battery Voltage Legend: Feb. 24, 2005 = state variable = measurement = command Switch Sensor Health Heater Heat Flow Camera Op Mode Camera Temperature Camera Thermal Mass R&TD DSAN M&C Temperature Sensor Health Ambient Temperature Sensor Scale Factor Sensor Bias Thermal Resistance 61

State Discovery Am I Done Yet, Have I Gone Too Far? • You’re done State Discovery Am I Done Yet, Have I Gone Too Far? • You’re done when everything you care about is accounted for as states or effects • Make models only as complex as needed; apply good engineering judgment • If a state doesn’t affect any state you care about, and you don’t care about the state, then you don’t need to model it (e. g. location of Venus for a Mars Lander) • If a state, command, measurement, or effect is purposely omitted because it is deemed insignificant, the reason should be documented • You’ve gone too far if the same state is represented in more than one state variable • Unique state representation ensures consistency and simplifies implementation Feb. 24, 2005 R&TD DSAN M&C 62

State Analysis Checklist • Start with an objective – a state that the control State Analysis Checklist • Start with an objective – a state that the control system needs to control • Add affecting states • Decide what states to combine or separate – Consider time derivatives, mathematical convenience, coestimation, co-control, and combined telemetry • Add measurements – Identify measurements that are informative about those states – Identify other states that affect the measurements – Be sure to include sensor health state and calibration state • Add commands – Identify any commands that affect the states – Identify other states that affect what the commands do – Be sure to include actuator health state and calibration state Feb. 24, 2005 R&TD DSAN M&C 63

State Analysis Continues with Modeling of Effects State Effects Model Measurement Model Command Model State Analysis Continues with Modeling of Effects State Effects Model Measurement Model Command Model October 23, 2003 R&TD DSAN M&C

State Effects Model • A state effects model describes the behavior of a physical State Effects Model • A state effects model describes the behavior of a physical state variable, including how other state variables (if any) affect it – It’s a function of true state – It’s a predictive model, based on physics • The model serves multiple purposes: simulation, estimation, control, goal elaboration, etc. State Effects Diagram A Model of the Effects: Switch Position Heater Health & Resistance R Battery Voltage V Feb. 24, 2005 Heater Heat Flow If switch is closed and heater is healthy then heat flow = k V 2 / R else heat flow = 0 R&TD DSAN M&C 65

Measurement Model for a Switch Sensor • A measurement model describes how one or Measurement Model for a Switch Sensor • A measurement model describes how one or more states affect a sensor’s measurement values – – It’s a function of all affecting states It’s a predictive model of what the sensor will produce The model is a requirement on the hardware Sensors often measure more than a single state variable Measurement Model: Table entries specifies measurement. State Effects Diagram Sensor Healthy Opened Switch Position Feb. 24, 2005 Switch Sensor Health Switch Position Openedmeas anything Closedmeas anything Tripped Switch Pos. Measurement Sensor Unhealthy Trippedmeas anything R&TD DSAN M&C 66

Measurement Model for a Temp’ Sensor • This model shows effects of continuous and Measurement Model for a Temp’ Sensor • This model shows effects of continuous and discrete states – It’s a predictive model of what the sensor will produce – Predicted values are in DN (data numbers), not engineering units – A measurement is meaningless without a measurement model • A measurement model holds information that an estimator uses to interpret measurements State Effects Diagram Temperature Measurement Camera Temperature Feb. 24, 2005 Temperature Sensor Health Sensor Scale Factor Sensor Bias Measurement Model: if temperature sensor healthy then measurement = scale factor (temperature + sensor bias) else measurement = 255 R&TD DSAN M&C 67

‘Direction’ of a Measurement Model • A measurement model takes state values as input ‘Direction’ of a Measurement Model • A measurement model takes state values as input and predicts measurements – – It describes the sensor’s transfer function It is EU to DN Can’t do this with DN to EU model Also show fault case sensor failed 255 DN, same model • However, an estimator needs to do the opposite – Given a measurement, estimate the states – Can invert previous measurement model to convert DN to EU: If measurement = 255 then sensor is unhealthy temperature = unknown else sensor is healthy temperature = (measurement / scale factor) - bias • Is this inverted model OK? Feb. 24, 2005 R&TD DSAN M&C 68

‘Direction’ of a Measurement Model (2) If measurement = 255 then sensor is unhealthy ‘Direction’ of a Measurement Model (2) If measurement = 255 then sensor is unhealthy temperature = unknown else sensor is healthy temperature = (measurement / scale factor) - bias • Bug: If temperature slowly rises to produce measurement of 255 then … – temperature suddenly unknown – healthy sensor is marked unhealthy • In general, measurement models are not invertible – Estimators often employ predictor-corrector algorithms (e. g. Kalman filters) or hypothesize-and-test algorithms (esp. for diagnosis) • State Analysis documents the forward model and discourages inversion Feb. 24, 2005 R&TD DSAN M&C 69

Measurements: Key Properties • Measurements are not States – A measurement provides evidence about Measurements: Key Properties • Measurements are not States – A measurement provides evidence about states – A measurement represents a moment in time – State is continuous in time and includes uncertainty • A measurement is a function of state (true state, not estimated state) – – This function is the Measurement Model Includes EU to DN conversion Includes measurement noise, latency Can be used to compare predict with actual measurement (compute residual) • Sensors always measure more than the intended state – Sensor health, bias, scale factor, and other “side effects” Feb. 24, 2005 R&TD DSAN M&C 70

Command Model • A command model describes effects of commands sent to an actuator Command Model • A command model describes effects of commands sent to an actuator – The effects depend on state when command issued – The model describes instantaneous effects • A command model informs… – Estimator design – Controller design Command Model close-cmd & actuator healthy State Effects Diagram Opened Switch Command Switch Actuator Health Feb. 24, 2005 Switch Position Closed open-cmd & actuator healthy Tripped R&TD DSAN M&C over current 71

Command Model: An Alternate Form • Command models can be described in more than Command Model: An Alternate Form • Command models can be described in more than one way • This equivalent model captures the same command effects • What’s important is that the model be reviewable Command Model: If switch actuator is healthy, then look up new state in table, else no change in state Switch command Open Close State Effects Diagram Switch Command Opened Switch Actuator Health Feb. 24, 2005 Switch Position R&TD DSAN M&C Closed Opened Closed (no change) Tripped Switch position Opened (no change) Opened (reset) Tripped (unchanged ) 72

The Model • When we say “The Model”, it means… State Variables State Effects The Model • When we say “The Model”, it means… State Variables State Effects Models Measurement Models Feb. 24, 2005 R&TD DSAN M&C Command Models 73

The Model More Than Just a Diagram • Captures physics of how state evolves The Model More Than Just a Diagram • Captures physics of how state evolves over time, and under the influence of other states, e. g. : – Increasing temperature when heater is on – Relation between heat flow and heater switch – How a sensor behaves in a fault mode • Physical assumptions must be explicitly specified, e. g. : – Flexibility of rover mast is assumed negligible • Information in the model is used in: – Estimators elaborations – Scheduling Controllers Goal Resource management etc • Traditional systems engineering approaches capture most of this information in multiple disparate artifacts, allowing for inconsistencies Feb. 24, 2005 R&TD DSAN M&C 74