Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Computational Elements of Robust Civil Infrastructure

Computing Research Institute

Purdue UniversityAhmed Sameh, George Cybenko,

Paul van Dooren, Kent Fuchs, Mete Sozen, et al.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Motivation• Civil infrastructure represents the single largest

investment in the United States, valued at over $20 trillion.

• While these systems are in a constant state of renewal, they are often required to withstand extreme loads caused by natural disasters and human intervention.

• High-rise structures, long-span bridges, dams, and pipelines are particularly vulnerable.

• The serviceability and safety of these structures can be vastly improved if damage can be detected and controlled in real-time.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Objectives• With the availability of reliable inexpensive sensors,

large-scale actuation devices, and computing and communication elements, the technology for active control of large structures exists, in principle.

• The goal of this ambitious project is to:– Enable effective design and economical

construction of highly robust smart structures.– Enhance robustness of existing structures by

suitably retrofitting them.– Predict and mitigate impact of catastrophic events,– Provide support for area-wide disaster

management plans.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

State-of-the-art in Controlled Structures

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Building Blocks of Smart Structures

Magnetorheostatic dampers can change their load bearing characteristics from fully solid to fully damping in milliseconds when exposed to magnetic fields.

Sensing/Computation/Communication elements - designed by part of our research team at Dartmouth. These units cost ~ $100 and are the size of a deck of cards. This is a rapidly evolving field and efforts are on to develop the next generation of devices here at Purdue.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Control Timelines

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Control Strategy

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Outstanding Challenges

• Building reliable inexpensive sensing/computation/communication/actuation (SCCA) units.

• Building a reliable network of SCCA units.• Structural modeling and model reduction.• Execution of the distributed control algorithm

with tight real-time constraints.• Supporting an area-wide disaster

management information network.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Sensing, Computation, Communication Units

• Current “smart brick” architecture integrates sensing, computation, and communication into a single unit, about the size of a deck of cards and costing ~ $100.

• The computation component is an Intel 80C51 microprocessor with a small ROM.

• Communication is provided by an RF transceiver operating at 915 MHz with Amplitude Shift Keying (ASK) Modulation.

• Sensing is supported through an iButton interface.• In addition, GPS interfaces provide global position.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Sensor/Actuator Networking

• Considerable prior work exists in sensor node design, energy-efficient communication, media access control (MAC), ad hoc routing, naming and self-organization, and data fusion.

• This problem, however, poses a set of unique challenges because of possibility of large number of network faults, tight bounds on latency, heterogeneity, security, and incomplete information, in addition to traditional aspects of scalability, adaptivity, robustness, and energy constraints.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Latency and Heterogeneity• The 250 ms bound on affecting control places

significant pressure on network latency for executing distributed sensing and control algorithms.

• Data priorities must be established based on message type, sensor type, sensor accuracy, and sensor location.

• These priorities must be supported by the MAC protocol - hybrid contention based/non-contention (reservation) schemes.

• Energy efficient routing protocols combined with low power device operation modes are necessary.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Adaptivity and Security

• Methods for evaluating and reducing routing latencies (pre-computed redundant routes).

• Periodic/on-demand self organization based on proximity, density, bandwidth, and priority.

• Methods for securing the sensor network - authentication, secure multiparty computations, testing and validation.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Real-Time Structure Control

• A full-scale control algorithm relying on a high-fidelity model cannot be expected to yield results (control vectors) within prescribed time bounds (~50ms).

• Our approach computes a reduced model off-line and to use adaptive techniques to tune this low-order model for possible time-dependent phenomena.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Real-Time Structure Control Consider the large-scale dynamical system

described by the second order system of equations of the form:

M(w,t)x’’(t) + D(w,t)x’(t) + K(w,t)x(t) =

Bff(t) + Bww(t) + Bee(t)

y(t) = C1x(t) + C2x’(t) + C3x’’(t)

• M: Mass matrix D: Damping matrix• K: Stiffness matrix x: State vector• f: Forcing function through actuators• e(t): Ground motion w(t): Wind/lateral excitation• y(t): Sensor observables

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Model Reduction

• The dimensionality of the state vector (and the control vector depending on f(t) can be very large). For this reason, we must first derive a reduced order model of the system:

M(w,t)u’’(t) + D(w,t)u’(t) + K(w,t)u(t) =

Bff(t) + Bww(t) + Bee(t)

y(t) = C1u(t) + C2u’(t) + C3u’’(t)

Here, u(t)(=Vx(t)) is the projected state on an appropriate space of lower dimension.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Sensor/Actuator Placement

• Given a large set of sensors and actuators, determine the location of a smaller subset of sensors and actuators such that the Hinf. norm of the subset is as close as possible to the norm of the original set.

• Requires computation of the Hankel singular values/solving dense Lyapunov equations and generalized eigenvalue problems.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Controlling the System

• The control vector f(t) = C(s)y(t) can be designed using optimal control, robust control, or Hinf control techniques.

• The problem of dealing with system uncertainties can be incorporated into robust/ Hinf control.

• Controller must also account for possible sensor/actuator failures. These are precomputed.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Other Issues in Control• System calibration: system characteristics may

change over time. For this reason, the system parameters must be periodically adapted (either using excitations from actuation or wind).

• Estimation and prediction: Often, we need to compute control vectors with incomplete sensed information.

• Real-time control adaptation: Large oscillations result in non-linear phenomena in the stiffness and damping matrices. Use a projected reduced order model via adaptive identification of variations in nominal model.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Fault Tolerance and Dependability

• System fault tolerance: consensus algorithms, redundancy, testing and diagnoses, recovery mechanisms.

• Algorithmic fault tolerance: Robust controllers, redundant computations, predictor/corrector methods.

• Software validation and testing: Extensive performance validation of all critical code components.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Development Plan• Component development and validation.• Simulation environment for examining system

behavior and response.• Physical validation at the Building Research Institute

at Tsukuba, Japan on reduced scale structures.• Instrumentation of actual structures (bridges in

Southern Indiana, wind affected buildings in Puerto Rico).

• Development of a disaster management plan in cooperation with the Southwest Indiana Regional Disaster Management Corp.

Ananth Grama, Computing Research Institute and Department of Computer Sciences, Purdue University

Advisors and Collaborators

• Profs. Moehle, Berkeley, and Otani (Tokyo, Earthquake Engg.)

• Prof. Golub, Problems in Modeling and Control, Stanford.

• Drs. Miyoshi and Scott (Sandia).• Dr. Shan (HP/Cooltown)• et alia.