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Mo Jamshidi Electrical and Computer Engineering Department and Autonomous Control

Control of Large-Scale Complex Systems – From Hierarchical to Autonomous and now to System of Systems. Mo Jamshidi Electrical and Computer Engineering Department and Autonomous Control Engineering (ACE) Center University of New Mexico, Albuquerque moj@wacong.org. OUT LINE.

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Mo Jamshidi Electrical and Computer Engineering Department and Autonomous Control

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  1. Control of Large-Scale Complex Systems – From Hierarchical to Autonomous and now to System of Systems Mo Jamshidi Electrical and Computer Engineering Department and Autonomous Control Engineering (ACE) Center University of New Mexico, Albuquerque moj@wacong.org

  2. OUTLINE Definition of a Large-Scale System Modeling of Large-Scale Systems HierarchicalControl Decentralized Control Applications System of Systems

  3. DEFINITION 1 A system is large in scale if it can be decomposed into subsystems. LSS Hierarchical Control … ss1 ss2 ss3 ssN

  4. DEFINITION 1, Cont’d. Pictorial representation of system decomposition and coordination, (a) An interconnected system; (b) a hierarchically structured system

  5. DEFINITION 2 A system is large in scale if concept of centrality no longer holds. y LSS u LSS uN CN u2 yN y1 Decentralized Control … C2 C1 y2 u1

  6. LSS is … Associated with three concepts: 1. Decomposition 2. Centrality 3. Complexity

  7. Modeling There are 3 classes of models for Large-scale systems: Aggregation Perturbation Descriptive variable

  8. Aggregation,cont’d. A 4th order system (left) has been approximated with 2nd order system (right) Key properties, like stability, needs to be preserved from system x to system z.

  9. Aggregation,cont’d. 2 Full (Original) Model: dx(t)/dt = Ax(t) + Bu(t) y(t) = Dx(t) z(t) = C x(t) Reduced Model: dz(t)/dt = Fz(t) + Gu(t) y(t) = Hz(t) C is aggregation matrix

  10. Balanced Aggregation Full: (A,B,D)  Reduced:(F,G,H) • Balanced Realization Aggregation : • Principle Component Analysis • (A,B,C) == > (Ab, Bb, Cb), where • Ab = A-1AS, Bb = S-1B, C = Cb S • S = LcUS–1/2 • U is orthogonal modal matrix • is the diagonal symmetric matrix of a certain eigenvalue / eigenvector problem Lc is a lower triangular Cholesky factros of controllability Grammian Gc of (A,B)

  11. Balanced Aggregation, Contd. Transformed matrices (Ab, Bb, Cb) represent an ordered diagonal set of modes with the most controllable and most observable mode appearing in location 1,1 of the matrices. Hence, F = Subset (Ab), G = Subset (Bb), etc. Matlab m files are available for all of the above manipulation of model reduction.

  12. PERTURBATION An perturbed model of a system is described by reduce model consisting of a structure after neglecting certain interactions within the model. Regular Perturbation– weak couplings SingularPerturbation – strong Coupling

  13. PERTURBATION, Cont’d. 2 SINGULAR Perturbation A mathematical process in which a system's variables are designated "slow" or "fast" in time-scale variations. dx/dt = Ax + Bu dxs/dt = Asxs + Bsu + Asfxf dxf/dt = Afxf + Bfu Approximation Fast variable

  14. PERTURBATION, Cont’d 3 SINGULAR Perturbation Boundary Layer Coorection for fast variables. Boundary layer correction for fast state z(t). ---, Ž(t); ——, Ž(t). + (t).

  15. Decentralized Controllers Taken from the theory of large-scale (complex) systems one can share the control action among a finite number of local controllers Input Output LARGE-SCALE SYSTEM un u1 . . . Controller 1 Controller n y1 yn

  16. Hierarchical Controllers Again, taken from the theory of large-scale (complex) systems one can share the control among a finite number of local controllers Supreme Coordinator interaction factor a1 {xn,un} state, control {x1,u1} an … Subsystem 1 (Coordinator) Subsystem n (Coordinator) … … Subsytem 1 Subsystem k Subsystem m Subsystem 1

  17. LISA - Advanced Avionics Systems for Dependable Computing in Future Space Exploration - Astrophysics Laser Interferometry Space Antenna (LISA)

  18. R Recovery Interval f p=0o 0 Observation Interval Deploy Interval D Scenario A: Hyperbolic (e>1) Flyby

  19. Scenario B: Elliptical Orbit of Planet with Hyperbolic Flyby of Moon IntervalArray ActivityConfiguration 1 Plan / Service Probes docked 2 Deploy Probes depart Mothership 3 Data Collection Probes free-fall / payload on 4 Recover Probes return to Mothership Interval 4: Recover θpE= 0o Fuzzy Transition From Hyperbolic to Elliptical Model θR θf Interval 1: Plan / Service θpH= 0o θ0 θD Interval 3: Observation Fuzzy Transition From Elliptical to Hyperbolic Model Interval 2: Deploy

  20. Scenario C: Continuous Elliptical (0<e<1) or Circular (e=0) Observation . θp= 0o Deploy Probes θp= 0o Maintain Formation - Adjust when formation bounds reached θp= 0o Recover Probes

  21. . . .Hierarchical System Structure . . . Level II Mothership Structure Message Center Mother Ship Agent • Self Preservation • Determine Phase of Operation Level IIa • Optimize ref. trajectory • Compute Thrust vector Trajectory Control Message Center Message Center Earth-link Comm • Maintain as specified • Manage momentum Attitude Control Message Center Message Center Cross-link Comm Sensor Control Message Center Message Center Traj & Attitude Determination Thruster Control Message Center Message Center Probe Docking Control Electrical Power System Message Center Message Center FDIR

  22. Message Center Probe Agent . . .Hierarchical System Structure . . . Probe Spacecraft Structure Level II • Self Preservation Level IIa • Optimize ref. trajectory • Compute Thrust vector Trajectory Control Message Center Message Center Cross-link Comm • Maintain as specified • Manage momentum Attitude Control Message Center Message Center Traj & Attitude Determination Sensor Control Message Center Message Center Probe Docking Control Thruster Control Message Center Message Center FDIR Electrical Power System Message Center

  23. SYSTEM OFSYSTEMS ENGINEERING A Future for … Large-Scale Systems And Systems Engineering

  24. OUTLINE • Introduction • What are Systems of Systems • System of System Characteristics • Distinction Between System Engineering and SoSE • Research Areas • SoS Examples • Concluding Remarks

  25. INTRODUCTION • Changing Aerospace and Defense Industry • Emphasis on “large-scale systems integration” • Customers seeking solutions to problems, not asking for specific vehicles • Emerging System of System Context • Mix of multiple systems capable of independent operation but interact with each other

  26. EMERGING CONTEXT: SYSTEM OF SYSTEMS • Meeting a need or set of needs with a mix of independently operating systems • New and existing aircraft, spacecraft, ground equipment, other independent systems • System of Systems Examples • Coast Guard Deepwater Program • FAA Air Traffic Management • Army Future Combat Systems _ Robotic Colonies, etc.,etc.

  27. WHAT ARE SYSTEM OF SYSTEMS? • Metasystems that are themselves comprised of multiple autonomous embedded complex systems that can be diverse in technology, context, operation, geography and conceptual frame. • An airplane is not SoS, an airport is a SoS. • Significant challenges: • Determining the appropriate mix of independent systems • The operation of a SoS occurs in an uncertain environment • Interoperability

  28. SYSTEM OF SYSTEM CHARACTERISTICS What distinguishes Systems of Systems from other large systems? • Operational Independence of the Elements • Managerial Independence of the Elements • Evolutionary Development • Emergent behaviors • Geographic Distribution

  29. Nature of SoSE Engineering Existing Complex Systems Exclusive, Autonomous, Local Transformation Operational Context Keating, et al., 2003

  30. System of Systems Interconnected, Integrated Mission, Global, Emergent Structure Integrated, Aligned, and Transforming Operational Context System of Systems Keating, et al., 2003

  31. System of Systems Engineering The design, deployment, operation, and transformation of metasystems that must function as an integrated complex system to produce desirable results. Keating, et. al 2003 Jamshidi, 2005

  32. SystemofSystems • SoS: A metasystem consisting of multiple autonomous embedded complex systems that can be diverse in: • Technology • Context • Operation • Geography • Conceptual frame • An airplane is not SoS, an airport is a SoS. • A robot is not a SoS, but a robotic colony is a SoS • Significant challenges: • Determining the appropriate mix of independent systems • The operation of a SoS occurs in an uncertain environment • Interoperability Keating, et al., 2003

  33. SystemofSystems Definitions SoS: No universally accepted definition 1. Operational & Mang. independence+Geographical Dist. + Emerging Behvr+Evol. Dev. (ML, Space) 2. Integration+Inter-Operability.+Optmiz. to enhance battlefield scenarios (ML) 3. Large scale + distributed Systems Leading to more complex systems (Private Enterprize) 4. Within the context of warfighting systems – Inter Op.+Com’d. Synergy+Cont.+ Comp.+ Comm. +Info. (C4I) & Intel. (ML) Keating, et al., 2003

  34. DISTINCTION BETWEEN SYSTEM ENGINEERING AND SoSE SoSE represents a necessary extension and evolution of traditional system engineering. • Greatly expanded SoS requirements for tiered levels of discipline and rigor. • Centralized control structure vs. de-centralized control structure • A typical individual system (well defined end state, fixed budget, well defined schedule, technical baselines, homogeneous) • A typical System of Systems (not well defined end state, periodic budget variations, heterogeneous )

  35. RESEARCH AREAS • Optimization, combinatorial problem solving and control • Important for design, architecting, and control of a System of Systems to ensure optimal performance to complete the assigned task or missions. • Non-deterministic assessment, and decision-making and design under uncertainty • Non-deterministic operating environments • Reliability prediction • Decision-making support for SoS • Which constituent systems provide which contributions? • Domain-specific modeling and simulation • Identify areas of potential risk, areas which require additional analysis • Concept of operation development,mission rehearsal,training of assets • Assist in optimizing the design and operation to better meet requirements

  36. EXAMPLES • Air Traffic Control • Personal Air Vehicles • Future Combat Cystem • Internet • Intelligent Transport Systems • US Coast Guard Integrated Deepwater System

  37. US COAST GUARD INTEGRATED DEEPWATER SYSTEM • The United States Coast Guard • Protect the public, the environment, and U.S. economic and security interests in any maritime region • International waters and America's coasts, ports, and inland waterways. • Missions • Maritime Security • Maritime Safety • Maritime Mobility • National Defense • Protection of Natural Resources

  38. US COAST GUARD INTEGRATED DEEPWATER SYSTEM • An integrated approach to upgrading existing assets while transitioning to newer, more capable platforms with improved systems for command, control,communications, computers, intelligence, surveillance, and reconnaissance and innovative logistics support. • Ensure compatibility and interoperability of deepwater asstes, while providing high levels of operational effectiveness.

  39. LSS vs SoS Models Modeling of Systems of Systems? TOP LSS TOP BOT. … BOT. LSS Traditional LSS Modeling SoSE Modeling Difficulty

  40. SystemofSystemsPROBLEM THEMES 1. Fragmented Perspectives 2. Lack of Rigorous Development 3. Lack of Theoretical Grounding 4. IT Dominance 5. Limitations of trad. SE single system focus 6. Whole Systems Analysis Keating, et al., 2003

  41. Thankyou.

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